papers.json

{
    "M004": {
        "abstract": "Using state-of-the-art deep learning (DL) models to diagnose cancer from histology data presents several challenges related to the nature and availability of labeled histology images, including image size, stain variations, and label ambiguity. In addition, cancer grading and the localization of regions of interest (ROIs) in such images normally rely on both image- and pixel-level labels, with the latter requiring a costly annotation process. Deep weakly-supervised object localization (WSOL) methods provide different strategies for low-cost training of DL models. Given only image-class annotations, these methods can be trained to simultaneously classify an image, and yield class activation maps (CAMs) for ROI localization. This paper provides a review of deep WSOL methods to identify and locate diseases in histology images, without the need for pixel-level annotations. We propose a taxonomy in which these methods are divided into bottom-up and top-down methods according to the information flow in models. Although the latter have seen only limited progress, recent bottom-up methods are currently  driving a lot of progress with the use of deep WSOL methods. Early works focused on designing different spatial pooling functions. However, those methods quickly peaked in term of localization accuracy and revealed a major limitation, namely, \u2013 the under-activation of CAMs, which leads to high false negative localization. Subsequent works aimed to alleviate this shortcoming and recover the complete object from the background, using different techniques such as perturbation, self-attention, shallow features, pseudo-annotation, and task decoupling.<br>In the present paper, representative deep WSOL methods from our taxonomy are also evaluated and compared in terms of classification and localization accuracy using two challenging public histology datasets \u2013 one for colon cancer (GlaS), and a second, for breast cancer (CAMELYON16). Overall, the results indicate poor localization performance, particularly for generic methods that were initially designed to process natural images. Methods designed to address the challenges posed by histology data often use priors such as ROI size, or additional pixel-wise supervision estimated from a pre-trained classifier, allowing them to achieve better results. However, all the methods suffer from high false positive/negative localization. Classification performance is mainly affected by the model selection process, which uses either the classification or the localization metric. Finally, four key challenges are identified in the application of deep WSOL methods in histology, namely, \u2013 under-/over-activation of CAMs, sensitivity to thresholding, and model selection \u2013 and research avenues are provided to mitigate them. Our code is publicly available at <a href='https://github.com/jeromerony/survey_wsl_histology'>https://github.com/jeromerony/survey_wsl_histology</a>",
        "authors": "J\u00e9r\u00f4me Rony, Soufiane Belharbi, Jose Dolz, Ismail, Ben Ayed, Luke McCaffrey, Eric Granger",
        "award": null,
        "id": "4",
        "melba": "True",
        "or_id": "",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W46",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey",
        "yt_full": ""
    },
    "M018": {
        "abstract": "We propose neural network layers that explicitly combine frequency and image feature representations and show that they can be used as a versatile building block for reconstruction from frequency space data. Our work is motivated by the challenges arising in MRI acquisition where the signal is a corrupted Fourier transform of the desired image. The proposed joint learning schemes enable both correction of artifacts native to the frequency space and manipulation of image space representations to reconstruct coherent image structures at every layer of the network. This is in contrast to most current deep learning approaches for image reconstruction that treat frequency and image space features separately and often operate exclusively in one of the two spaces. We demonstrate the advantages of joint convolutional learning for a variety of tasks, including motion correction, denoising, reconstruction from undersampled acquisitions, and combined undersampling and motion correction on simulated and real world multicoil MRI data. The joint models produce consistently high quality output images across all tasks and datasets. When integrated into a state of the art unrolled optimization network with physics-inspired data consistency constraints for undersampled reconstruction, the proposed architectures significantly improve the optimization landscape, which yields an order of magnitude reduction of training time. This result suggests that joint representations are particularly well suited for MRI signals in deep learning networks. Our code and pretrained models are publicly available at <a href='https://github.com/nalinimsingh/interlacer'>https://github.com/nalinimsingh/interlacer</a>.",
        "authors": "Nalini M., Singh, Juan Eugenio, Iglesias, Elfar Adalsteinsson, Adrian V., Dalca, Polina Golland",
        "award": null,
        "id": "18",
        "melba": "True",
        "or_id": "",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W45",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis",
        "yt_full": ""
    },
    "O006": {
        "abstract": "Interpreting deep learning models typically relies on post-hoc saliency map techniques. However, these techniques often fail to serve as actionable feedback to clinicians, and they do not directly explain the decision mechanism. Here, we propose an inherently interpretable model that combines the feature extraction capabilities of deep neural networks with advantages of sparse linear models in interpretability. Our approach relies on straightforward but effective changes to a deep bag-of-local-features model (BagNet). These modifications lead to fine-grained and sparse class evidence maps which, by design, correctly reflect the model's decision mechanism. Our model is particularly suited for tasks which rely on characterising regions of interests that are very small and distributed over the image. In this paper, we focus on the detection of Diabetic Retinopathy, which is characterised by the progressive presence of small retinal lesions on fundus images. We observed good classification accuracy despite our added sparseness constraint. In addition, our model precisely highlighted retinal lesions relevant for the disease grading task and excluded irrelevant regions from the decision mechanism. The results suggest our sparse BagNet model can be a useful tool for clinicians as it allows efficient inspection of the model predictions and facilitates clinicians' and patients' trust.",
        "authors": "Kerol R. Djoumessi Donteu, Indu Ilanchezian, Laura K\u00fchlewein, Hanna Faber, Christian F. Baumgartner, Bubacarr Bah, Philipp Berens, Lisa M. Koch",
        "award": "",
        "id": "6",
        "melba": "False",
        "or_id": "us8BFTsWOq",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "W08",
        "schedule": "Wednesday, July 12: Oral session 8 - Computer-assisted diagnosis \u2014 14:00\u201315:00\nWednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Sparse Activations for Interpretable Disease Grading",
        "yt_full": ""
    },
    "O007": {
        "abstract": "Breast cancer is the most commonly diagnosed cancer and the use of artificial intelligence (AI) to help diagnose the disease from digital pathology images has the potential to greatly improve patient outcomes. However, current methods for detecting, segmenting, and sub-typing breast neoplasms and other proliferative lesions often rely on costly and time-consuming manual annotation efforts, which can be impractical for large-scale datasets. In this work, we propose an annotation-free learning framework to jointly detect, segment, and subtype breast neoplasms. Our approach leverages top-k multiple instance learning to train an initial neoplasm detection backbone network from weakly-labeled whole slide images, which is then used to automatically generate pixel-level pseudo-labels for whole slides with only one subtype. A second network is trained using these pseudo-labels, and slide-level classification is performed by training an aggregator network that fuses the embeddings from both backbone networks. We trained and validated our framework on large-scale datasets with more than 100k whole slide images and demonstrate its effectiveness on tasks including breast neoplasms detection, segmentation, and subtyping.",
        "authors": "Adam Casson, Siqi Liu, Ran A Godrich, Hamed Aghdam, Brandon Rothrock, Kasper Malfroid, Christopher Kanan, Thomas Fuchs",
        "award": "",
        "id": "7",
        "melba": "False",
        "or_id": "rXVtHHFLRIz",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "W26",
        "schedule": "Wednesday, July 12: Oral session 7 - Segmentation 2 \u2014 9:30\u201310:15\nWednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Joint Breast Neoplasm Detection and Subtyping using Multi-Resolution Network Trained on Large-Scale H&E Whole Slide Images with Weak Labels",
        "yt_full": ""
    },
    "O021": {
        "abstract": "The main benefit of unsupervised anomaly detection is the ability to identify arbitrary, rare data instances of pathologies even in the absence of training labels or sufficient examples of the rare class(es). In the clinical workflow, such methods have the potential to assist in screening and pre-filtering exams for potential pathologies and thus meaningfully support radiologists. Even though much work has been done on using auto-encoders (AE) for anomaly detection, there are still two critical challenges to overcome: First, learning compact and detailed representations of the healthy distribution is cumbersome. Recent work shows that AEs can reconstruct some types of anomalies even better than actual samples from the training distribution. Second, while the majority of unsupervised algorithms are tailored to detect hyperintense lesions on FLAIR brain MR scans, recent improvements in basic intensity thresholding techniques have outperformed them.  Moreover, we found that even state-of-the-art (SOTA) AEs fail to detect several classes of non-hyperintense anomalies on T1w brain MRIs, such as brain atrophy, edema, or resections. In this work, we propose reversed AEs (RA) to generate pseudo-healthy reconstructions and localize various brain pathologies. We extensively evaluate our method on T1w brain scans and increase the detection of global pathology and artefacts from 73.1 to 89.4 AUROC and the amount of detected local pathologies from 52.6% to 86.0% compared to SOTA methods.",
        "authors": "Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Julia A Schnabel",
        "award": "",
        "id": "21",
        "melba": "False",
        "or_id": "8ojx-Ld3yjR",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "M06",
        "schedule": "Monday, July 10: Oral session 2 - Unsupervised/weakly supervised methods \u2014 14:00\u201315:00\nMonday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening",
        "yt_full": ""
    },
    "O024": {
        "abstract": "This paper explores training medical vision-language models (VLMs) -- where the visual and language inputs are embedded into a common space -- with a particular focus on scenarios where training data is limited, as is often the case in clinical datasets. We explore several candidate methods to improve low-data performance, including: (i) adapting generic pre-trained models to novel image and text domains (i.e.\\ medical imaging and reports) via unimodal self-supervision; (ii) using local (e.g.\\ GLoRIA) \\& global (e.g. InfoNCE) contrastive loss functions as well as a combination of the two; (iii) extra supervision during VLM training, via: (a) image- and text-only self-supervision, and (b) creating additional positive image-text pairs for training through augmentation and nearest-neighbour search.  Using text-to-image retrieval as a benchmark, we evaluate the performance of these methods with variable sized training datasets of paired chest X-rays and radiological reports.  Combined, they significantly improve retrieval compared to fine-tuning CLIP, roughly equivalent to training with $10\\times$ the data. A similar pattern is found in the downstream task classification of CXR-related conditions with our method outperforming CLIP and also BioVIL, a strong CXR VLM benchmark, in the zero-shot and linear probing settings. We conclude with a set of recommendations for researchers aiming to train vision-language models on other medical imaging modalities when training data is scarce. To facilitate further research, we will make our code and models publicly available.  ",
        "authors": "Rhydian Windsor, Amir Jamaludin, Timor Kadir, Andrew Zisserman",
        "award": "",
        "id": "24",
        "melba": "False",
        "or_id": "2XVITHcQCfj",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "T08",
        "schedule": "Tuesday, July 11: Oral session 5 - Semi-supervised/self-supervised methods \u2014 14:00\u201315:00\nTuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime",
        "yt_full": ""
    },
    "O026": {
        "abstract": "Multiplex Immunohistochemistry (mIHC) is a cost-effective and accessible method for in situ labeling of multiple protein biomarkers in a tissue sample. By assigning a different stain to each biomarker, it allows the visualization of different types of cells within the tumor vicinity for downstream analysis. However, to detect different types of stains in a given mIHC image is a challenging problem, especially when the number of stains is high. Previous deep-learning-based methods mostly assume full supervision; yet the annotation can be costly. In this paper, we propose a novel unsupervised stain decomposition method to detect different stains simultaneously. Our method does not require any supervision, except for color samples of different stains. A main technical challenge is that the problem is underdetermined and can have multiple solutions. To conquer this issue, we propose a novel inversion regulation technique, which eliminates most undesirable solutions. On a 7-plexed IHC image dataset, the proposed method achieves high quality stain decomposition results without human annotation.",
        "authors": "Shahira Abousamra, Danielle Fassler, Jiachen Yao, Rajarsi R. Gupta, Tahsin Kurc, Luisa Escobar-Hoyos, Dimitris Samaras, Kenneth Shroyer, Joel Saltz, Chao Chen",
        "award": "",
        "id": "26",
        "melba": "False",
        "or_id": "J0VD-I2IOOg",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "M02",
        "schedule": "Monday, July 10: Oral session 2 - Unsupervised/weakly supervised methods \u2014 14:00\u201315:00\nMonday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Unsupervised Stain Decomposition via Inversion Regulation for Multiplex Immunohistochemistry Images",
        "yt_full": ""
    },
    "O029": {
        "abstract": "Concerns about the reproducibility of deep learning research are more prominent than ever, with no clear solution in sight. The Medical Imaging with Deep Learning (MIDL) conference has made advancements in employing empirical rigor with regards to reproducibility by advocating open access, and recently also recommending authors to make their code public---both aspects being adopted by the majority of the conference submissions.  We have evaluated all accepted full paper submissions to MIDL between 2018 and 2022 using established, but adjusted guidelines addressing the reproducibility and quality of the public repositories.  The evaluations show that publishing repositories and using public datasets are becoming more popular, which helps traceability, but the quality of the repositories shows room for improvement in every aspect. Merely 22% of all submissions contain a repository that was deemed repeatable using our evaluations.  From the commonly encountered issues during the evaluations, we propose a set of guidelines for machine learning-related research for medical imaging applications, adjusted specifically for future submissions to MIDL. We presented our results to future MIDL authors who were eager to continue an open discussion on the topic of code reproducibility.",
        "authors": "Attila Simk\u00f3, Anders Garpebring, Joakim Jonsson, Tufve Nyholm, Tommy L\u00f6fstedt",
        "award": "",
        "id": "29",
        "melba": "False",
        "or_id": "_P59zCfXOt",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "M07",
        "schedule": "Monday, July 10: MIDL board special session \u2014 10:30\u201311:00\nMonday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\nWednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/O029.pdf",
        "title": "Reproducibility of the Methods in Medical Imaging with Deep Learning.",
        "yt_full": "https://youtu.be/bNSsm0ptYmQ"
    },
    "O037": {
        "abstract": "Due to the low signal-to-noise ratio and limited resolution of functional MRI data, and the high complexity of natural images, reconstructing a visual stimulus from human brain fMRI measurements is a challenging task. In this work, we propose a novel approach for this task, which we call Cortex2Image, to decode visual stimuli with high semantic fidelity and rich fine-grained detail. In particular, we train a surface-based convolutional network model that maps from brain response to semantic image features first (Cortex2Semantic).  We then combine this model with a high-quality image generator (Instance-Conditioned GAN) to train another mapping from brain response to fine-grained image features using a variational approach (Cortex2Detail). Image reconstructions obtained by our proposed method achieve state-of-the-art semantic fidelity, while yielding good fine-grained similarity with the ground-truth stimulus. Our code is available on \\url{https://github.com/zijin-gu/meshconv-decoding.git}.",
        "authors": "Zijin Gu, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu",
        "award": "",
        "id": "37",
        "melba": "False",
        "or_id": "V5vvti2Y9PA",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "T05",
        "schedule": "Tuesday, July 11: Oral session 4 - Neuroimaging \u2014 9:00\u201310:15\nTuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Decoding natural image stimuli from fMRI data with a surface-based convolutional network",
        "yt_full": ""
    },
    "O039": {
        "abstract": "Three-dimensional segmentation in magnetic resonance images (MRI), which reflects the true shape of the objects, is challenging since high-resolution isotropic MRIs are rare and typical MRIs are anisotropic, with the out-of-plane dimension having a much lower resolution. A potential remedy to this issue lies in the fact that often multiple sequences are acquired on different planes. However, in practice, these sequences are not orthogonal to each other, limiting the applicability of many previous solutions to reconstruct higher-resolution images from multiple lower-resolution ones. We propose a novel deep learning-based solution to generating high-resolution masks from multiple low-resolution images. Our method combines segmentation and unsupervised registration networks by introducing two new regularizations to make registration and segmentation reinforce each other. Finally, we introduce a multi-view fusion method to generate high-resolution target object masks. The experimental results on two datasets show the superiority of our methods. Importantly, the advantage of not using high-resolution images in the training process makes our method applicable to a wide variety of MRI segmentation tasks.",
        "authors": "Hanxue Gu, Hongyu He, Roy Colglazier, Jordan Axelrod, Robert French, Maciej A Mazurowski",
        "award": "",
        "id": "39",
        "melba": "False",
        "or_id": "oi5psB9R_l",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "M05",
        "schedule": "Monday, July 10: Oral session 1 - Segmentation 1 \u2014 9:30\u201310:00\nMonday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs",
        "yt_full": ""
    },
    "O047": {
        "abstract": "Tagged magnetic resonance imaging (MRI) has been used for decades to observe and quantify the detailed motion of deforming tissue. However, this technique faces several challenges such as tag fading, large motion, long computation times, and difficulties in obtaining diffeomorphic incompressible flow fields. To address these issues, this paper presents a novel unsupervised phase-based 3D motion estimation technique for tagged MRI. We introduce two key innovations. First, we apply a sinusoidal transformation to the harmonic phase input, which enables end-to-end training and avoids the need for phase interpolation. Second, we propose a Jacobian determinant-based learning objective to encourage incompressible flow fields for deforming biological tissues.  Our method efficiently estimates 3D motion fields that are accurate, dense, and approximately diffeomorphic and incompressible. The efficacy of the method is assessed using human tongue motion during speech, and includes both healthy controls and patients that have undergone glossectomy. We show that the method outperforms existing approaches, and also exhibits improvements in speed, robustness to tag fading, and large tongue motion. The code is available.",
        "authors": "Zhangxing Bian, Fangxu Xing, Jinglun Yu, Muhan Shao, Yihao Liu, Aaron Carass, Jonghye Woo, Jerry L Prince",
        "award": "",
        "id": "47",
        "melba": "False",
        "or_id": "jkSC4UHHVzy",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "M08",
        "schedule": "Monday, July 10: Oral session 2 - Unsupervised/weakly supervised methods \u2014 14:00\u201315:00\nMonday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "DRIMET: Deep Registration-based 3D Incompressible Motion Estimation in Tagged-MRI with Application to the Tongue",
        "yt_full": ""
    },
    "O058": {
        "abstract": "Grading precancerous lesions on whole slide images is a challenging task: the continuous space of morphological phenotypes makes clear-cut decisions between different grades often difficult, leading to low inter- and intra-rater agreements. More and more Artificial Intelligence (AI) algorithms are developed to help pathologists perform and standardize their diagnosis. However, those models can render their prediction without consideration of the ambiguity of the classes and can fail without notice which prevent their wider acceptance in a clinical context.  In this paper, we propose a new score to measure the confidence of AI models in grading tasks. Our confidence score is specifically adapted to ordinal output variables, is versatile and does not require extra training or additional inferences nor particular architecture changes.  Comparison to other popular techniques such as Monte Carlo Dropout and deep ensembles shows that our method provides state-of-the art results, while being simpler, more versatile and less computationally intensive.  The score is also easily interpretable and consistent with real life hesitations of pathologists. We show that the score is capable of accurately identifying mispredicted slides and that accuracy for high confidence decisions is significantly higher than for low-confidence decisions (gap in AUC of 17.1\\% on the test set).  We believe that the proposed confidence score could be leveraged by pathologists directly in their workflow and assist them on difficult tasks such as grading precancerous lesions.",
        "authors": "Melanie Lubrano, Ya\u00eblle Bellahsen Harrar, Rutger RH Fick, C\u00e9cile Badoual, Thomas Walter",
        "award": "",
        "id": "58",
        "melba": "False",
        "or_id": "DA1hOTvcMWa",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "W06",
        "schedule": "Wednesday, July 12: Oral session 8 - Computer-assisted diagnosis \u2014 14:00\u201315:00\nWednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Simple and Efficient Confidence Score for Grading Whole Slide Images",
        "yt_full": ""
    },
    "O063": {
        "abstract": "Accuracy validation of cortical thickness measurement is a difficult problem due to the lack of ground truth data. To address this need, many methods have been developed to synthetically induce gray matter (GM) atrophy in an MRI via deformable registration, creating a set of images with known changes in cortical thickness. However, these methods often cause blurring in atrophied regions, and cannot simulate realistic atrophy within deep sulci where cerebrospinal fluid (CSF) is obscured or absent. In this paper, we present a solution using a self-supervised inpainting model to generate CSF in these regions and create images with more plausible GM/CSF boundaries. Specifically, we introduce a novel, 3D GAN model that incorporates patch-based dropout training, edge map priors, and sinusoidal positional encoding, all of which are established methods previously limited to 2D domains. We show that our framework significantly improves the quality of the resulting synthetic images and is adaptable to unseen data with fine-tuning. We also demonstrate that our resulting dataset can be employed for accuracy validation of cortical segmentation and thickness measurement.",
        "authors": "Jiacheng Wang, Kathleen Larson, Ipek Oguz",
        "award": "",
        "id": "63",
        "melba": "False",
        "or_id": "HR1GtDQnuw",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "T06",
        "schedule": "Tuesday, July 11: Oral session 5 - Semi-supervised/self-supervised methods \u2014 14:00\u201315:00\nTuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Self-Supervised CSF Inpainting for Improved Accuracy Validation of Cortical Surface Analyses ",
        "yt_full": ""
    },
    "O069": {
        "abstract": "We focus on the problem of producing well-calibrated out-of-distribution (OOD) detectors, in order to enable safe deployment of medical image classifiers. Motivated by the difficulty of curating suitable calibration datasets, synthetic augmentations have become highly prevalent for inlier/outlier specification. While there have been rapid advances in data augmentation techniques, this paper makes a striking finding that the space in which the inliers and outliers are synthesized, in addition to the type of augmentation, plays a critical role in calibrating OOD detectors. Using the popular energy-based OOD detection framework, we find that the optimal protocol is to synthesize latent-space inliers along with diverse pixel-space outliers. Based on empirical studies with multiple medical imaging benchmarks, we demonstrate that our approach consistently leads to superior OOD detection ($15\\% - 35\\%$ in AUROC) over the state-of-the-art in a variety of open-set recognition settings.",
        "authors": "Vivek Narayanaswamy, Yamen Mubarka, Rushil Anirudh, Deepta Rajan, Andreas Spanias, Jayaraman J. Thiagarajan",
        "award": "",
        "id": "69",
        "melba": "False",
        "or_id": "RU7fr0-M8N",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "T12",
        "schedule": "Tuesday, July 11: Oral session 6 - Synthesis \u2014 16:00\u201316:45\nTuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors",
        "yt_full": ""
    },
    "O077": {
        "abstract": "We harness a Transformer-based model and a pre-training procedure for fingerprinting on fMRI data, to enhance the accuracy of stress predictions. Our model, called MetricFMRI, first optimizes a pixel-based reconstruction loss. In a second unsupervised training phase, a triplet loss is used to encourage fMRI sequences of the same subject to have closer representations, while sequences from different subjects are pushed away from each other. Finally, supervised learning is used for the target task, based on the learned representation. We evaluate the performance of our model and other alternatives and conclude that the triplet training for the fingerprinting task is key to the improved accuracy of our method for the task of stress prediction. To obtain insights regarding the learned model, gradient-based explainability techniques are used, indicating that sub-cortical brain regions that are known to play a central role in stress-related processes are highlighted by the model. ",
        "authors": "Gony Rosenman, Itzik Malkiel, Ayam Greental, Talma Hendler, Lior Wolf",
        "award": "",
        "id": "77",
        "melba": "False",
        "or_id": "W9qI8DwoUFF",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "T09",
        "schedule": "Tuesday, July 11: Oral session 4 - Neuroimaging \u2014 9:00\u201310:15\nTuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Pre-Training Transformers for Fingerprinting to Improve Stress Prediction in fMRI",
        "yt_full": ""
    },
    "O088": {
        "abstract": "Colon resection is often the treatment of choice for colorectal cancer (CRC) patients. However, especially for minimally invasive cancer, such as pT1, simply removing the polyps may be enough to stop cancer progression. Different histopathological risk factors such as tumor grade and invasion depth currently found the basis for the need for colon resection in pT1 CRC patients. Here, we investigate two additional risk factors, tumor budding and lymphocyte infiltration at the invasive front, which are known to be clinically relevant. We capture the spatial layout of tumor buds and T-cells and use graph-based deep learning to investigate them as potential risk predictors. Our pT1 Hotspot Tumor Budding T-cell Graph (pT1-HBTG) dataset consists of 626 tumor budding hotspots from 575 patients. We propose and compare three different graph structures, as well as combinations of the node labels. The best-performing Graph Neural Network architecture is able to increase specificity by 20% compared to the currently recommended risk stratification based on histopathological risk factors, without losing any sensitivity. We believe that using a graph-based analysis can help to assist pathologists in making risk assessments for pT1 CRC patients, and thus decrease the number of patients undergoing potentially unnecessary surgery. Both the code and dataset are made publicly available.",
        "authors": "Linda Studer, JM Bokhorst, I Nagtegaal, Inti Zlobec, Heather Dawson, Andreas Fischer",
        "award": "",
        "id": "88",
        "melba": "False",
        "or_id": "ruaXPgZCk6i",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "M12",
        "schedule": "Monday, July 10: Oral session 3 - Graph-based methods \u2014 16:00\u201316:45\nMonday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Tumor Budding T-cell Graphs: Assessing the Need for Resection in pT1 Colorectal Cancer Patients",
        "yt_full": ""
    },
    "O100": {
        "abstract": "Deep learning models benefit from training with a large dataset (labeled or unlabeled). Following this motivation, we present an approach to learn a deep learning model for the automatic segmentation of Organs at Risk (OARs) in cervical cancer radiation treatment from a large clinically available dataset of Computed Tomography (CT) scans containing data inhomogeneity, label noise, and missing annotations. We employ simple heuristics for automatic data cleaning to minimize data inhomogeneity and label noise. Further, we develop a semi-supervised learning approach utilizing a teacher-student setup, annotation imputation, and uncertainty-guided training to learn in presence of missing annotations. Our experimental results show that learning from a large dataset with our approach yields a significant improvement in the test performance despite missing annotations in the data. Further, the contours generated from the segmentation masks predicted by our model are found to be equally clinically acceptable as manually generated contours.",
        "authors": "Monika Grewal, Dustin van Weersel, Henrike Westerveld, Peter Bosman, Tanja Alderliesten",
        "award": "",
        "id": "100",
        "melba": "False",
        "or_id": "uPRFWdz03_",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "M01",
        "schedule": "Monday, July 10: Oral session 1 - Segmentation 1 \u2014 9:30\u201310:00\nMonday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Learning Clinically Acceptable Segmentation of Organs at Risk in Cervical Cancer Radiation Treatment from Clinically Available Annotations",
        "yt_full": ""
    },
    "O125": {
        "abstract": "Single-cell high-throughput microscopy images contain key biological information underlying normal and pathological cellular processes. Image-based analysis and profiling are powerful and promising for extracting this information but are made difficult due to substantial complexity and heterogeneity in cellular phenotype. Hand-crafted methods and machine learning models are popular ways to extract cell image information. Representations extracted via machine learning models, which often exhibit good reconstruction performance, lack biological interpretability. Hand-crafted representations, on the contrary, have clear biological meanings and thus are interpretable. Whether these hand-crafted representations can also generate realistic images is not clear. In this paper, we propose a CellProfiler to image (CP2Image) model that can directly generate realistic cell images from CellProfiler representations. We also demonstrate most biological information encoded in the CellProfiler representations is well-preserved in the generating process. This is the first time hand-crafted representations be shown to have generative ability and provide researchers with an intuitive way for their further analysis.",
        "authors": "Yanni Ji, Marie Cutiongco, Bj\u00f8rn Sand Jensen, Ke Yuan",
        "award": "",
        "id": "125",
        "melba": "False",
        "or_id": "4LqtcKZoKeB",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "T10",
        "schedule": "Tuesday, July 11: Oral session 6 - Synthesis \u2014 16:00\u201316:45\nTuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "CP2Image: Generating high-quality single-cell images using CellProfiler representations",
        "yt_full": ""
    },
    "O130": {
        "abstract": "In this paper, we propose a novel two-component loss for biomedical image segmentation tasks called the Instance-wise and Center-of-Instance (ICI) loss, a loss function that addresses the instance imbalance problem commonly encountered when using pixel-wise loss functions such as the Dice loss. The Instance-wise component improves the detection of small instances or ``blobs\" in image datasets with both large and small instances. The Center-of-Instance component improves the overall detection accuracy. We compared the ICI loss with two existing losses, the Dice loss and the blob loss, in the task of stroke lesion segmentation using the ATLAS R2.0 challenge dataset from MICCAI 2022. Compared to the other losses, the ICI loss provided a better balanced segmentation, and significantly outperformed the Dice loss with an improvement of $1.7-3.7\\%$ and the blob loss by $0.6-5.0\\%$ in terms of the Dice similarity coefficient on both validation and test set, suggesting that the ICI loss is a potential solution to the instance imbalance problem.",
        "authors": "Febrian Rachmadi, Charissa Poon, henrik skibbe",
        "award": "",
        "id": "130",
        "melba": "False",
        "or_id": "8o83y0_YtE",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "W03",
        "schedule": "Wednesday, July 12: Oral session 7 - Segmentation 2 \u2014 9:30\u201310:15\nWednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Improving Segmentation of Objects with Varying Sizes in Biomedical Images using Instance-wise and Center-of-Instance Segmentation Loss Function",
        "yt_full": ""
    },
    "O134": {
        "abstract": "We present Roto-Translation Equivariant Spherical Deconvolution (RT-ESD), an $E(3)\\times SO(3)$ equivariant framework for sparse deconvolution of volumes where each voxel contains a spherical signal. Such 6D data naturally arises in diffusion MRI (dMRI), a medical imaging modality widely used to measure microstructure and structural connectivity. As each dMRI voxel is typically a mixture of various overlapping structures, there is a need for blind deconvolution to recover crossing anatomical structures such as white matter tracts. Existing dMRI work takes either an iterative or deep learning approach to sparse spherical deconvolution, yet it typically does not account for relationships between neighboring measurements. This work constructs equivariant deep learning layers which respect to symmetries of spatial rotations, reflections, and translations, alongside the symmetries of voxelwise spherical rotations. As a result, RT-ESD improves on previous work across several tasks including fiber recovery on the DiSCo dataset, deconvolution-derived partial volume estimation on real-world in vivo human brain dMRI, and improved downstream reconstruction of fiber tractograms on the Tractometer dataset. The code will be released.",
        "authors": "Axel Elaldi, Guido Gerig, Neel Dey",
        "award": "",
        "id": "134",
        "melba": "False",
        "or_id": "lri_iAbpn_r",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "T07",
        "schedule": "Tuesday, July 11: Oral session 4 - Neuroimaging \u2014 9:00\u201310:15\nTuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "E(3) x SO(3)-Equivariant Networks for Spherical Deconvolution in Diffusion MRI",
        "yt_full": ""
    },
    "O140": {
        "abstract": "The reconstruction of graph representations from Images (Image-to-graph) is a frequent task, especially vessel graph extraction from biomedical images. Traditionally, this problem is tackled by a two-stage process: segmentation followed by skeletonization. However, the ambiguity in the heuristic-based pruning of the centerline graph from the skeleta makes it hard to achieve a compact yet faithful graph representation. Recently, \\textit{Relationformer} proposed an end-to-end solution to extract graphs directly from images. However, it does not consider edge features, particularly radius information, which is crucial in many applications such as flow simulation. Further, Relationformer predicts only patch-based graphs. In this work, we address these two shortcomings. We propose a task-specific token, namely radius-token, which explicitly focuses on capturing radius information between two nodes. Second, we propose an efficient algorithm to infer a large 3D graph from patch inference. Finally, we show experimental results on a synthetic vessel dataset and achieve the first 3D complete graph prediction. Code is available at \\url{https://github.com/****}. ",
        "authors": "Chinmay Prabhakar, Suprosanna Shit, Johannes C. Paetzold, Ivan Ezhov, Rajat Koner, Hongwei Li, Florian Sebastian Kofler, bjoern menze",
        "award": "",
        "id": "140",
        "melba": "False",
        "or_id": "X_AJqHfE1H",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "M14",
        "schedule": "Monday, July 10: Oral session 3 - Graph-based methods \u2014 16:00\u201316:45\nMonday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Vesselformer: Towards Complete 3D Vessel Graph Generation from Images",
        "yt_full": ""
    },
    "O159": {
        "abstract": "We present a novel approach to transcranial ultrasound computed tomography that utilizes normalizing flows to improve the speed of imaging and provide Bayesian uncertainty quantification. Our method combines physics-informed methods and data-driven methods to accelerate the reconstruction of the final image. We make use of a physics-informed summary statistic to incorporate the known ultrasound physics with the goal of compressing large incoming observations. This compression enables efficient training of the normalizing flow and standardizes the size of the data regardless of imaging configurations. The combinations of these methods results in fast uncertainty-aware image reconstruction that generalizes to a variety of transducer configurations. We evaluate our approach with in silico experiments and demonstrate that it can significantly improve the imaging speed while quantifying uncertainty. We validate the quality of our image reconstructions by comparing against the traditional physics-only method and also verify that our provided uncertainty is calibrated with the error. ",
        "authors": "Rafael Orozco, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, Felix Johan Herrmann",
        "award": "",
        "id": "159",
        "melba": "False",
        "or_id": "LoJG-lUIlk",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "T01",
        "schedule": "Tuesday, July 11: Oral session 4 - Neuroimaging \u2014 9:00\u201310:15\nTuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification",
        "yt_full": ""
    },
    "O160": {
        "abstract": "Longitudinal studies, where a series of images from the same set of individuals are acquired at different time-points, represent a popular technique for studying and characterizing temporal dynamics in biomedical applications. The classical approach for longitudinal comparison involves normalizing for nuisance variations, such as image orientation or contrast differences, via pre-processing. Statistical analysis is, in turn, conducted to detect changes of interest, either at the individual or population level. This classical approach can suffer from pre-processing issues and limitations of the statistical modeling. For example, normalizing for nuisance variation might be hard in settings where there are a lot of idiosyncratic changes. In this paper, we present a simple machine learning-based approach that can alleviate these issues. In our approach, we train a deep learning model (called PaIRNet, for Pairwise Image Ranking Network) to compare pairs of longitudinal images, with or without supervision. In the self-supervised setup, for instance, the model is trained to temporally order the images, which requires learning to recognize time-irreversible changes. Our results from four datasets demonstrate that PaIRNet can be very effective in localizing and quantifying meaningful longitudinal changes while discounting nuisance variation. Our code is available at \\url{https://github.com/heejong-kim/learning-to-compare-longitudinal-images}",
        "authors": "Heejong Kim, Mert R. Sabuncu",
        "award": "",
        "id": "160",
        "melba": "False",
        "or_id": "l17YFzXLP53",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "T04",
        "schedule": "Tuesday, July 11: Oral session 5 - Semi-supervised/self-supervised methods \u2014 14:00\u201315:00\nTuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Learning to Compare Longitudinal Images",
        "yt_full": ""
    },
    "O177": {
        "abstract": "Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion correction techniques jointly optimize estimates of the image and the motion parameters. In this paper, we use a deep network to reduce the joint image-motion parameter search to a search over rigid motion parameters alone. Our network produces a reconstruction as a function of two inputs: corrupted k-space data and motion parameters. We train the network using simulated, motion-corrupted k-space data generated from known motion parameters. At test-time, we estimate unknown motion parameters by minimizing a data consistency loss between the motion parameters, the network-based image reconstruction given those parameters, and the acquired measurements. Intra-slice motion correction experiments on simulated and realistic 2D fast spin echo brain MRI achieve high reconstruction fidelity while retaining the benefits of explicit data consistency-based optimization.",
        "authors": "Nalini M Singh, Neel Dey, Malte Hoffmann, Bruce Fischl, Elfar Adalsteinsson, Robert Frost, Adrian V Dalca, Polina Golland",
        "award": "",
        "id": "177",
        "melba": "False",
        "or_id": "KolMbwNBNGv",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "T03",
        "schedule": "Tuesday, July 11: Oral session 4 - Neuroimaging \u2014 9:00\u201310:15\nTuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Data Consistent Deep Rigid MRI Motion Correction",
        "yt_full": ""
    },
    "O190": {
        "abstract": "We present a physics-enhanced implicit neural representation (INR) for ultrasound (US) imaging that learns tissue properties from overlapping US sweeps. Our proposed method leverages a ray-tracing-based neural rendering for novel view US synthesis. Recent publications demonstrated that INR models could encode a representation of a three-dimensional scene from a set of two-dimensional US frames. However, these models fail to consider the view-dependent changes in appearance and geometry intrinsic to US imaging. In our work, we discuss direction-dependent changes in the scene and show that a physics-inspired rendering improves the fidelity of US image synthesis. In particular, we demonstrate experimentally that our proposed method generates geometrically accurate B-mode images for regions with ambiguous representation owing to view-dependent differences of the US images. We conduct our experiments using simulated B-mode US sweeps of the liver and acquired US sweeps of a spine phantom tracked with a robotic arm. The experiments corroborate that our method generates US frames that enable consistent volume compounding from previously unseen views. To the best of our knowledge, the presented work is the first to address view-dependent US image synthesis using INR.",
        "authors": "Magdalena Wysocki, Mohammad Farid Azampour, Christine Eilers, Benjamin Busam, Mehrdad Salehi, Nassir Navab",
        "award": "",
        "id": "190",
        "melba": "False",
        "or_id": "x4McMBwVyi",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "T14",
        "schedule": "Tuesday, July 11: Oral session 6 - Synthesis \u2014 16:00\u201316:45\nTuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging",
        "yt_full": ""
    },
    "O191": {
        "abstract": "The electrocardiogram (ECG) is one of the most commonly used non-invasive, convenient medical monitoring tools that assist in the clinical diagnosis of heart diseases. Recently, deep learning (DL) techniques, particularly self-supervised learning (SSL), have demonstrated great potential in the classification of ECGs. SSL pre-training has achieved competitive performance with only a small amount of annotated data after fine-tuning. However, current SSL methods rely on the availability of annotated data and are unable to predict labels not existing in fine-tuning datasets. To address this challenge, we propose \\textbf{M}ultimodal \\textbf{E}CG-\\textbf{T}ext \\textbf{S}elf-supervised pre-training (METS), \\textbf{the first work} to utilize the auto-generated clinical reports to guide ECG SSL pre-training. We use a trainable ECG encoder and a frozen language model to embed paired ECGs and automatically machine-generated clinical reports separately, then the ECG embedding and paired report embedding are compared with other unpaired embeddings. In downstream classification tasks, METS achieves around 10\\% improvement in performance without using any annotated data via zero-shot classification, compared to other supervised and SSL baselines that rely on annotated data. Furthermore, METS achieves the highest recall and F1 scores on the MIT-BIH dataset, despite MIT-BIH containing different classes of ECGs compared to the pre-trained dataset. The extensive experiments have demonstrated the advantages of using ECG-Text multimodal self-supervised learning in terms of generalizability and effectiveness.",
        "authors": "Jun Li, Che Liu, Sibo Cheng, Rossella Arcucci, Shenda Hong",
        "award": "",
        "id": "191",
        "melba": "False",
        "or_id": "UAr59yTUWR2",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "W04",
        "schedule": "Wednesday, July 12: Oral session 8 - Computer-assisted diagnosis \u2014 14:00\u201315:00\nWednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Frozen Language Model Helps ECG Zero-Shot Learning",
        "yt_full": ""
    },
    "O198": {
        "abstract": "To fully extract the feature information of lung parenchyma in Chest X-ray images and realize the auxiliary diagnosis of COVID-19 pneumonia, this paper proposed an end-to-end deep learning model, which is mainly composed of object detection, depth feature generation, and multi-channel fusion classification. Firstly, the convolutional neural network (CNN) and region proposal network (RPN)-based object detection module was adopted to detect chest cavity region of interest (ROI). Then, according to the obtained coordinate information of ROI and the convolution feature map of original image, the new convolution feature maps of ROI were obtained with number of 13. By screening 4 representative feature maps form 4 convolution layers with different receptive fields and combining with original ROI image, the 5-dimensional (5D) feature maps were generated as the multi-channel input of classification module. Moreover, in each channel of classification module, three pyramidal recursive MLPs were employed to achieve cross-scale and cross-channel feature analysis. Finally, the correlation analysis of multi-channel output was realized by bi-directional long short memory (Bi-LSTM) module, and the auxiliary diagnosis of pneumonia disease was realized through fully connected layer and SoftMax function. Experimental results show that the proposed model has better classification performance and diagnosis effect than previous methods, with great clinical application potential.",
        "authors": "Yiwen Liu, Wenyu Xing, Mingbo Zhao, MINGQUAN LIN",
        "award": "",
        "id": "198",
        "melba": "False",
        "or_id": "2cgTLIy1Zx",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "W02",
        "schedule": "Wednesday, July 12: Oral session 8 - Computer-assisted diagnosis \u2014 14:00\u201315:00\nWednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "An end-to-end framework for diagnosing COVID-19 pneumonia via Parallel Recursive MLP module and Bi-LTSM correlation",
        "yt_full": ""
    },
    "O216": {
        "abstract": "Generative statistical models have a wide range of applications in the modelling of anatomies. In-silico clinical trials of medical devices, for instance, require the development of virtual populations of anatomy that capture enough variability while remaining plausible. Model construction and use are heavily influenced by the correspondence problem and establishing shape matching over a large number of training data.This study focuses on generating virtual cohorts of left ventricle geometries resembling different-sized shape populations, suitable for in-silico experiments. We present an unsupervised data-driven probabilistic generative model for shapes. This framework incorporates an attention-based shape matching procedure using graph neural networks, coupled with a $\\beta-$VAE generation model, eliminating the need for initial shape correspondence. Left ventricle shapes derived from cardiac magnetic resonance images available in the UK Biobank are utilized for training and validating the framework. We investigate our method\u2019s generative capabilities in terms of generalisation and specificity and show that it is able to synthesise virtual populations of realistic shapes with volumetric measurements in line with actual clinical indices. Moreover, results show our method outperforms joint registration-PCA-based models.",
        "authors": "Soodeh Kalaie, Andrew J. Bulpitt, Alejandro F. Frangi, Ali Gooya",
        "award": "",
        "id": "216",
        "melba": "False",
        "or_id": "Ao0D2HMB8P",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "M10",
        "schedule": "Monday, July 10: Oral session 3 - Graph-based methods \u2014 16:00\u201316:45\nMonday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "A Geometric Deep Learning Framework for Generation of Virtual Left Ventricles as Graphs",
        "yt_full": ""
    },
    "O221": {
        "abstract": "Image augmentations are quintessential for effective visual representation learning across self-supervised learning techniques. While augmentation strategies for natural imaging have been studied extensively, medical images are vastly different from their natural counterparts. Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent. To address this challenge, in this study, we systematically assess the effect of various augmentations on the quality and robustness of the learned representations. We train and evaluate Siamese Networks for abnormality detection on chest X-Rays across three large datasets (MIMIC-CXR, CheXpert and VinDr-CXR). We investigate the efficacy of the learned representations through experiments involving linear probing, fine-tuning, zero-shot transfer, and data efficiency. Finally, we identify a set of augmentations that yield robust representations that generalize well to both out-of-distribution data and diseases, while outperforming supervised baselines using just zero-shot transfer and linear probes by up to 20%.",
        "authors": "Rogier Van der Sluijs, Nandita Bhaskhar, Daniel Rubin, Curtis Langlotz, Akshay S Chaudhari",
        "award": "",
        "id": "221",
        "melba": "False",
        "or_id": "xkmhsBITaCw",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "T02",
        "schedule": "Tuesday, July 11: Oral session 5 - Semi-supervised/self-supervised methods \u2014 14:00\u201315:00\nTuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays",
        "yt_full": ""
    },
    "O236": {
        "abstract": "Smoothing hard-label assignments has emerged as a popular strategy in training discriminative models. Nevertheless, most existing approaches are typically designed for classification tasks, ignoring underlying properties of dense prediction problems, such as medical image segmentation. First, these strategies often ignore the spatial relations between a given pixel and its neighbours. And second, the image context associated with each label is overlooked, which can convey important information about potential errors or ambiguities in the segmentation masks. To address these limitations, we propose in this work geodesic label smoothing (GeoLS), which integrates image information into the label smoothing process by leveraging the geodesic distance transform of the images. As the resulting label assignment is based on the computed geodesic map, class-wise relationships in the soft-labels are better modeled, as it considers image gradients at the boundary of two or more categories. Furthermore, spatial pixel-wise relationships are captured in the geodesic distance transform, integrating richer information than resorting to the Euclidean distance between pixels. We evaluate our method on two publicly available segmentation benchmarks and compare them to popular segmentation loss functions that directly modify the standard hard-label assignments. The proposed geodesic label smoothing improves the segmentation accuracy over existing soft-labeling strategies, demonstrating the validity of integrating image information into the label smoothing process. The code to reproduce our results is available at: https://github.com/anonymous35783578/GeoLS.",
        "authors": "Sukesh Adiga Vasudeva, Jose Dolz, Herve Lombaert",
        "award": "",
        "id": "236",
        "melba": "False",
        "or_id": "mTIP1bkmR0q",
        "oral": "True",
        "pmlr_url": "",
        "poster_loc": "W01",
        "schedule": "Wednesday, July 12: Oral session 7 - Segmentation 2 \u2014 9:30\u201310:15\nWednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "GeoLS: Geodesic Label Smoothing for Image Segmentation",
        "yt_full": ""
    },
    "P002": {
        "abstract": "Crohn\u2019s Disease (CD) and Ulcerative Colitis (UC) are the two main Inflammatory Bowel Disease (IBD) types. We developed interpretable deep learning models to identify histolog- ical disease features for both CD and UC using only endoscopic labels. We explored fine- tuning and end-to-end training of two state-of-the-art self-supervised models for predicting three different endoscopic categories (i) CD vs UC (AUC=0.87), (ii) normal vs lesional (AUC=0.81), (iii) low vs high disease severity score (AUC=0.80). With the support of a pathologist, we explored the relationship between endoscopic labels, model predictions and histological evaluations qualitatively and quantitatively and identified cases where the pathologist\u2019s descriptions of inflammation were consistent with regions of high attention. In parallel, we used a model trained on the Colon Nuclei Identification and Counting (CoNIC) dataset to predict and explore 6 cell populations. We observed consistency between areas enriched with the predicted immune cells in biopsies and the pathologist\u2019s feedback on the attention maps. Finally, we identified several cell level features indicative of disease severity in CD and UC. These models can enhance our understanding about the pathology behind IBD and can shape our strategies for patient stratification in clinical trials.",
        "authors": "Ricardo Mokhtari, Azam Hamidinekoo, Daniel James Sutton, Arthur Lewis, Bastian Angermann, Ulf Gehrmann, P\u00e5l Lundin, Hibret Adissu, Junmei Cairns, Jessica Neisen, Emon Khan, Daniel Marks, Nia Khachapuridze, Talha Qaiser, Nikolay Burlutskiy",
        "award": "",
        "id": "2",
        "melba": "False",
        "or_id": "m-f1SNDhde",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M09",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Interpretable histopathology-based prediction of disease relevant features in Inflammatory Bowel Disease biopsies using weakly-supervised deep learning",
        "yt_full": ""
    },
    "P003": {
        "abstract": "Active learning promises to improve annotation efficiency by iteratively selecting the most important data to be annotated first. However, we uncover a striking contradiction to this promise: at the first few choices, active learning fails to select data as efficiently as random selection. We identify this as the cold start problem in active learning, caused by a biased and outlier initial query. This paper seeks to address the cold start problem and develops a novel active querying strategy, named HaCon, that can exploit the three advantages of contrastive learning: (1) no annotation is required; (2) label diversity is ensured by pseudo-labels to mitigate bias; (3) typical data is determined by contrastive features to reduce outliers. Experiments on three public medical datasets show that HaCon not only significantly outperforms existing active querying strategies but also surpasses random selection by a large margin. Code is available at https://github.com/liangyuch/CSVAL.",
        "authors": "Liangyu Chen, Yutong Bai, Siyu Huang, Yongyi Lu, Bihan Wen, Alan Yuille, Zongwei Zhou",
        "award": "",
        "id": "3",
        "melba": "False",
        "or_id": "5iSBMWm3ln",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T13",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Making Your First Choice: To Address Cold Start Problem in Medical Active Learning",
        "yt_full": ""
    },
    "P008": {
        "abstract": "Magnetic resonance (MR) images from multiple sources often show differences in image contrast related to acquisition settings or the used scanner type. For long-term studies, longitudinal comparability is essential but can be impaired by these contrast differences, leading to biased results when using automated evaluation tools. This study presents a diffusion model-based approach for contrast harmonization. We use a data set consisting of scans of 18 Multiple Sclerosis patients and 22 healthy controls. Each subject was scanned in two MR scanners of different magnetic field strengths (1.5 T and 3 T), resulting in a paired data set that shows scanner-inherent differences. We map images from the source contrast to the target contrast for both directions, from 3 T to 1.5 T and from 1.5 T to 3 T. As we only want to change the contrast, not the anatomical information, our method uses the original image to guide the image-to-image translation process by adding structural information. The aim is that the mapped scans display increased comparability with scans of the target contrast for downstream tasks. We evaluate this method for the task of segmentation of cerebrospinal fluid, grey matter and white matter. Our method achieves good and consistent results for both directions of the mapping.",
        "authors": "Alicia Durrer, Julia Wolleb, Florentin Bieder, Tim Sinnecker, Matthias Weigel, Robin Sandkuehler, Cristina Granziera, \u00d6zg\u00fcr Yaldizli, Philippe C. Cattin",
        "award": "",
        "id": "8",
        "melba": "False",
        "or_id": "Xs_Hd23_PP",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W07",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Diffusion Models for Contrast Harmonization of Magnetic Resonance Images",
        "yt_full": ""
    },
    "P009": {
        "abstract": "Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks. They do, however, require a large amount of computational resources. This limits their application to medical tasks, where we often deal with large 3D volumes, like high-resolution three-dimensional data. In this work, we present a number of different ways to reduce the resource consumption for 3D diffusion models and apply them to a dataset of 3D images. The main contribution of this paper is the memory-efficient patch-based diffusion model PatchDDM, which can be applied to the total volume during inference while the training is performed only on patches. Without limiting the application of the proposed diffusion model for image generation, we evaluate the method on the tumor segmentation task of the BraTS2020 dataset and demonstrate that we can generate meaningful three-dimensional segmentations.",
        "authors": "Florentin Bieder, Julia Wolleb, Alicia Durrer, Robin Sandkuehler, Philippe C. Cattin",
        "award": "",
        "id": "9",
        "melba": "False",
        "or_id": "neXqIGpO-tn",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M13",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Denoising Diffusion Models for Memory-efficient Processing of 3D Medical Images",
        "yt_full": ""
    },
    "P010": {
        "abstract": "Detection of pathologies is a fundamental task in medical imaging and the evaluation of algorithms that can perform this task automatically is crucial. However, current object detection metrics for natural images do not reflect the specific clinical requirements in pathology detection sufficiently. To tackle this problem, we propose Robust Detection Outcome (RoDeO); a novel metric for evaluating algorithms for pathology detection in medical images, especially in chest X-rays. RoDeO evaluates different errors directly and individually, and reflects clinical needs better than current metrics. Extensive evaluation on the ChestX-ray8 dataset shows the superiority of our metrics compared to existing ones. We released the code at [https://github.com/FeliMe/RoDeO](https://github.com/FeliMe/RoDeO) and published RoDeO as pip package ($rodeometric$).",
        "authors": "Felix Meissen, Philip M\u00fcller, Georgios Kaissis, Daniel Rueckert",
        "award": "",
        "id": "10",
        "melba": "False",
        "or_id": "zyiJi4sJ7dZ",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T29",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\nWednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P010.pdf",
        "title": "Robust Detection Outcome: A Metric for Pathology Detection in Medical Images",
        "yt_full": "https://youtu.be/A_pOOc8lKFY"
    },
    "P011": {
        "abstract": "One major problem in deep learning-based solutions for medical imaging is the drop in performance when a model is tested on a data distribution different from the one that it is trained on.  Adapting the source model to target data distribution at test-time is an efficient solution for the data-shift problem. Previous methods solve this by adapting the model to target distribution by using techniques like entropy minimization or regularization. In these methods, the models are still updated by back-propagation using an unsupervised loss on complete test data distribution. In real-world clinical settings, it makes more sense to adapt a model to a new test image on-the-fly and avoid model update during inference due to privacy concerns and lack of computing resource at deployment. To this end, we propose a new setting - On-the-Fly Adaptation which is zero-shot and episodic i.e., the model is adapted to a single image at a time and also does not perform any back-propagation during test-time). To achieve this, we propose a new framework called Adaptive UNet where each convolutional block is equipped with an adaptive batch normalization layer to adapt the features with respect to a domain code. The domain code is generated using a pre-trained encoder trained on a large corpus of medical images. During test-time, the model takes in just the new test image and generates a domain code to adapt the features of source model according to the test data. We validate the performance on both 2D and 3D data distribution shifts where we get a better performance compared to previous test-time adaptation methods.",
        "authors": "Jeya Maria Jose Valanarasu, Pengfei Guo, Vibashan VS, Vishal M. Patel",
        "award": "",
        "id": "11",
        "melba": "False",
        "or_id": "UQDalTzrEg",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W05",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "On-the-Fly Test-time Adaptation for Medical Image Segmentation",
        "yt_full": ""
    },
    "P012": {
        "abstract": "Deep Learning (DL) based methods for magnetic resonance (MR) image reconstruction have been shown to produce superior performance. However, previous methods either only leverage under-sampled data or require a paired fully-sampled auxiliary MR sequence to perform guidance-based reconstruction. Consequently, existing approaches neglect to explore attention mechanisms that can transfer texture from reference data to under-sampled data within a single MR sequence, which either limits the performance of these approaches or increases the difficulty of data acquisition. In this paper, we propose a novel $\\textbf{T}$exture $\\textbf{T}$ransformer $\\textbf{M}$odule ($\\textbf{TTM}$) for the reference-based MR image reconstruction. The TTM facilitates joint feature learning across under-sampled and reference data, so feature correspondences can be discovered by attention and accurate texture features can be leveraged during reconstruction. Notably, TTM can be stacked on prior MRI reconstruction methods to improve their performance. In addition, a $\\textbf{R}$ecurrent $\\textbf{T}$ransformer $\\textbf{R}$econstruction backbone ($\\textbf{RTR}$) is proposed to further improve the performance in a unified framework. Extensive experiments demonstrate the effectiveness of TTM and show that RTR can achieve the state-of-the-art results on multiple datasets. Implementation code and pre-trained weights will be made public after the review process.  ",
        "authors": "Pengfei Guo, Vishal M. Patel",
        "award": "",
        "id": "12",
        "melba": "False",
        "or_id": "EoEWcHFHJ1W",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M11",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Reference-based MRI Reconstruction Using Texture Transformer",
        "yt_full": ""
    },
    "P014": {
        "abstract": "The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless. The DIP overfits to noise if not early stopped, or optimized via a regularized objective. We build on the regularized fine-tuning of a pretrained DIP, by adopting a novel strategy that restricts the learning to the adaptation of singular values. The proposed SVD-DIP uses ad hoc convolutional layers whose pretrained parameters are decomposed via the singular value decomposition. Optimizing the DIP then solely consists in the fine-tuning of the singular values, while keeping the left and right singular vectors fixed. We thoroughly validate the proposed method on real-measured \u03bcCT data of a lotus root as well as two medical datasets (LoDoPaB and Mayo). We report significantly improved stability of the DIP optimization, by overcoming the overfitting to noise.",
        "authors": "Marco Nittscher, Michael Falk Lameter, Riccardo Barbano, Johannes Leuschner, Bangti Jin, Peter Maass",
        "award": "",
        "id": "14",
        "melba": "False",
        "or_id": "ivC7VP2mof",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T11",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction",
        "yt_full": ""
    },
    "P015": {
        "abstract": "Cell detection in histopathology images facilitates clinical diagnosis, and deep learning methods have been applied to the detection problem with substantially improved performance. However, cell detection methods based on deep learning usually require a large number of annotated training samples, which are costly and time-consuming to obtain, and it is desirable to develop methods where detection networks can be adequately trained with only a few annotated training samples. Since unlabeled data is much less expensive to obtain, it is possible to address this problem with semi-supervised learning, where abundant unlabeled data is combined with the limited annotated training samples for network training. In this work, we propose a semi-supervised object detection method for cell detection in histopathology images, which is based on and improves the mean teacher framework. In standard mean teacher, the detection results on unlabeled data given by the teacher model can be noisy, which may negatively impact the learning of the student model. To address this problem, we propose to suppress the noise in the detection results of the teacher model by mixing the unlabeled training images with labeled training images of which the ground truth detection results are available. In addition, we propose to further incorporate a loss term that is robust to noise when the the student model learns from the teacher model. To evaluate the proposed method, experiments were performed on a publicly available dataset for multi-class cell detection, and the experimental results show that our method improves the performance of cell detection in histopathology images in the semi-supervised setting.",
        "authors": "Ziqi Wen, Chuyang Ye",
        "award": "",
        "id": "15",
        "melba": "False",
        "or_id": "PEWigppmw3b",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P015.pdf",
        "title": "A Robust Mean Teacher Framework for Semi-Supervised Cell Detection in Histopathology Images",
        "yt_full": "https://youtu.be/SKtcmrrD6xo"
    },
    "P016": {
        "abstract": "Deep learning systems have been proposed to improve the objectivity and efficiency of Ki-67 PI scoring. The challenge is that deep learning techniques, while very accurate, suffer from reduced performance when applied to out-of-domain data. This is a critical challenge for clinical translation, as models are typically trained using data available to the vendor, which is not from the target domain. To address this challenge, this study proposes a domain adaptation pipeline that employs an unsupervised framework to generate silver standard (pseudo) labels in the target domain, which is used to augment the gold standard (GS) source domain data. Five training regimes were tested on two validated Ki-67 scoring architectures (UV-Net and piNET), (1) SS Only: trained on target silver standard (SS) labels, (2) GS Only: trained on source GS labels, (3) Mixed: trained on target SS and source GS labels, (4) GS+SS: trained on source GS labels and fine-tuned on target SS labels, and our proposed method (5) SS+GS: trained on source SS labels and fine-tuned on source GS labels. The SS+GS method yielded significantly ($p<0.05$) higher PI accuracy ($95.9\\%$) and more consistent results compared to the GS Only model on target data.  Analysis of t-SNE plots showed features learned by the SS+GS models are more aligned for source and target data which results in improved generalization. The proposed pipeline provides an efficient method for learning the target distribution  without the need for manual annotations, which are time-consuming and costly to generate for medical images. This framework can be applied to any target site as a per-laboratory calibration method, for widescale deployment.",
        "authors": "Amanda Dy, Ngoc-Nhu Jennifer Nguyen, Seyed Hossein Mirjahanmardir, Dimitrios Androutsos, Melanie Dawe, Anthony Fyles, Wei Shi, Fei-Fei Liu, Susan Done, April Khademi",
        "award": "",
        "id": "16",
        "melba": "False",
        "or_id": "-ahfrpMo9ui",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M15",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Domain Adaptation using Silver Standard Labels for Ki-67 Scoring in Digital Pathology A Step Closer to Widescale Deployment",
        "yt_full": ""
    },
    "P023": {
        "abstract": "Self-supervised learning has attracted increasing attention as it learns data-driven representation from data without annotations. Vision transformer-based autoencoder (ViT-AE) by He et al. (2021) is a recent self-supervised learning technique that employs a patch-masking strategy to learn a meaningful latent space. In this paper, we focus on improving ViT-AE (nicknamed ViT-AE++) for a more effective representation of both 2D and 3D medical images. We propose two new loss functions to enhance the representation during the training stage. The first loss term aims to improve self-reconstruction by considering the structured dependencies and hence indirectly improving the representation. The second loss term leverages contrastive loss to directly optimize the representation from two randomly masked views. As an independent contribution, we extended ViT-AE++ to a 3D fashion for volumetric medical images.  We extensively evaluate ViT-AE++ on both natural images and medical images, demonstrating consistent improvement over vanilla ViT-AE and its superiority over other contrastive learning approaches.",
        "authors": "Chinmay Prabhakar, Hongwei Li, Jiancheng Yang, Suprosanna Shit, Benedikt Wiestler, bjoern menze",
        "award": "",
        "id": "23",
        "melba": "False",
        "or_id": "2Aoi0VKPOWT",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T15",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image Representations",
        "yt_full": ""
    },
    "P031": {
        "abstract": "Multi-scale representations have proven to be a powerful tool since they can take into account both the fine-grained details of objects in an image as well as the broader context. Inspired by this, we propose a novel dual-branch transformer network that operates on two different scales to encode global contextual dependencies while preserving local information. To learn in a self-supervised fashion, our approach considers the semantic dependency that exists between different scales to generate a supervisory signal for inter-scale consistency and also imposes a spatial stability loss within the scale for self-supervised content clustering. While intra-scale and inter-scale consistency losses aim to increase features similarly within the cluster, we propose to include a cross-entropy loss function on top of the clustering score map to effectively model each cluster distribution and increase the decision boundary between clusters. Iteratively our algorithm learns to assign each pixel to a semantically related cluster to produce the segmentation map. Extensive experiments on skin lesion and lung segmentation datasets show the superiority of our method compared to the state-of-the-art (SOTA) approaches. ",
        "authors": "Sanaz Karimijafarbigloo, Reza Azad, Amirhossein Kazerouni, Dorit Merhof",
        "award": "",
        "id": "31",
        "melba": "False",
        "or_id": "pp2raGSU3Wx",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P031.pdf",
        "title": "MS-Former: Multi-Scale Self-Guided Transformer for Medical Image Segmentation",
        "yt_full": "https://youtu.be/8zK8o7b8Bw4"
    },
    "P032": {
        "abstract": "Incorporating either rotation equivariance or scale equivariance into CNNs has proved to be effective in improving models\u2019 generalization performance. However, jointly integrating rotation and scale equivariance into CNNs has not been widely explored. Digital histology imaging of biopsy tissue can be captured at arbitrary orientation and magnification and stored at different resolutions, resulting in cells appearing in different scales. When conventional CNNs are applied to histopathology image analysis, the generalization performance of models is limited because 1) a part of the parameters of filters are trained to fit rotation transformation, thus decreasing the capability of learning other discriminative features; 2) fixed-size filters trained on images at a given scale fail to generalize to those at different scales. To deal with these issues, we propose the Rotation-Scale Equivariant Steerable Filter (RSESF), which incorporates steerable filters and scale-space theory. The RSESF contains copies of filters that are linear combinations of Gaussian filters, whose direction is controlled by directional derivatives and whose scale parameters are trainable but constrained to span disjoint scales in successive layers of the network. Extensive experiments on two gland segmentation datasets demonstrate that our method outperforms other approaches, with much fewer trainable parameters and fewer GPU resources required. The source code is available at: https://github.com/ynulonger/RSESF.",
        "authors": "Yilong Yang, Srinandan Dasmahapatra, Sasan Mahmoodi",
        "award": "",
        "id": "32",
        "melba": "False",
        "or_id": "A0MyiAwE_E4",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P032.pdf",
        "title": "Rotation-Scale Equivariant Steerable Filters",
        "yt_full": "https://youtu.be/8PtZLo2Ihkw"
    },
    "P033": {
        "abstract": "Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within these datasets (a process known as re-identification). To address this issue, we propose an AI-powered medical imaging retrieval framework called DeepBrainPrint, which is designed to retrieve brain MRI scans of the same patient. Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations. First, we use a combination of self-supervised and supervised paradigms to create an effective brain fingerprint from MRI scans that can be used for real-time image retrieval. Second, we use a special weighting function to guide the training and improve model convergence. Third, we introduce new imaging transformations to improve retrieval robustness in the presence of intensity variations (i.e. different scan contrasts), and to account for age and disease progression in patients. We tested DeepBrainPrint on a large dataset of T1-weighted brain MRIs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and on a synthetic dataset designed to evaluate retrieval performance with different image modalities. Our results show that DeepBrainPrint outperforms previous methods, including simple similarity metrics and more advanced contrastive deep learning frameworks.",
        "authors": "Lemuel Puglisi, Arman Eshaghi, Geoff Parker, Frederik Barkhof, Daniel C. Alexander, Daniele Ravi",
        "award": "",
        "id": "33",
        "melba": "False",
        "or_id": "i5khDI1te1M",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M16",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification",
        "yt_full": ""
    },
    "P035": {
        "abstract": "Deformable image registration is a crucial component in the analysis of motion in time series. In medical data, the deformation fields are often predictable to a certain degree: the muscles and other tissues causing the motion-of-interest form shapes that may be used as a geometric prior. Using an Implicit Neural Representation to parameterize a deformation field allows the coordinate space to be chosen arbitrarily. We propose to curve this coordinate space around anatomical structures that influence the motion in our time series, yielding a space that is aligned with the expected motion. The geometric information is therefore explicitly encoded into the neural representation, reducing the complexity of the optimized deformation function. We design and evaluate this concept using an abdominal 3D cine-MRI dataset, where the motion of interest is bowel motility. We align the coordinate system of the neural representations with automatically extracted centerlines of the small intestine. We show that explicitly encoding the intestine geometry in the neural representations improves registration accuracy for bowel loops with active motility when compared to registration using neural representations in the original coordinate system. Additionally, we show that registration accuracy can be further improved using a model that combines a neural representation in image coordinates with a separate neural representation that operates in the proposed tangent coordinate system. This approach may improve the efficiency of deformable registration when describing motion-of-interest that is influenced by the shape of anatomical structures.",
        "authors": "Louis van Harten, Rudolf Leonardus Mirjam Van Herten, Jaap Stoker, Ivana Isgum",
        "award": "",
        "id": "35",
        "melba": "False",
        "or_id": "Pj9vtDIzSCE",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T16",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Deformable Image Registration with Geometry-informed Implicit Neural Representations",
        "yt_full": ""
    },
    "P038": {
        "abstract": "Magnetic resonance imaging (MRI) is a common non-invasive imaging technique with high soft tissue contrast. Different MRI modalities are used for the diagnosis of various conditions including T1-weighted and T2-weighted MRI. In this paper, we introduce MTSR-MRI, a novel method that can not only upscale low-resolution scans but also translates between the T1-weighted  and T2-weighted modalities. This will potentially reduce the scan time or repeat scans by taking low-resolution inputs in one modality and returning plausible high-resolution output in another modality. Due to the ambiguity that persists in image-to-image translation tasks, we consider the distribution of possible outputs in a conditional generative setting. The mapping is distilled in a low-dimensional latent distribution which can be randomly sampled at test time, thus allowing us to generate multiple plausible high-resolution outputs from a given low-resolution input. We validate the proposed method on the BraTS-18 dataset qualitatively and quantitatively using a variety of similarity measures. The implementation of this work will be available at https://github.com/AvirupJU/MTSR-MRI . ",
        "authors": "Avirup Dey, Mehran Ebrahimi",
        "award": "",
        "id": "38",
        "melba": "False",
        "or_id": "mUPIsk20oGt",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W09",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "MTSR-MRI: Combined Modality Translation and Super-Resolution of Magnetic Resonance Images",
        "yt_full": ""
    },
    "P046": {
        "abstract": "Pretraining on large natural image classification datasets such as ImageNet has aided model development on data-scarce 2D medical tasks. 3D medical tasks often have much less data than 2D medical tasks, prompting practitioners to rely on pretrained 2D models to featurize slices. However, these 2D models have been surpassed by 3D models on 3D computer vision benchmarks since they do not natively leverage cross-sectional or temporal information. In this study, we explore whether natural video pretraining for 3D models can enable higher performance on smaller datasets for 3D medical tasks. We demonstrate video pretraining improves the average performance of seven 3D models on two chest CT datasets, regardless of finetuning dataset size, and that video pretraining allows 3D models to outperform 2D baselines. Lastly, we observe that pretraining on the large-scale out-of-domain Kinetics dataset improves performance more than pretraining on a typically-sized in-domain CT dataset. Our results show consistent benefits of video pretraining across a wide array of architectures, tasks, and training dataset sizes, supporting a shift from small-scale in-domain pretraining to large-scale out-of-domain pretraining for 3D medical tasks.",
        "authors": "Alexander Ke, Shih-Cheng Huang, Chloe P O'Connell, Michal Klimont, Serena Yeung, Pranav Rajpurkar",
        "award": "",
        "id": "46",
        "melba": "False",
        "or_id": "zhpP7zluLBk",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P046.pdf",
        "title": "Video pretraining advances 3D deep learning on chest CT tasks",
        "yt_full": ""
    },
    "P048": {
        "abstract": "Automatic 3-dimensional tooth segmentation on intraoral scans (IOS) plays a pivotal role in computer-aided orthodontic treatments. In practice, deploying existing well-trained models to different medical centers suffers from two main problems: (1) the data distribution shifts between existing and new centers, (2) the data in the existing center is usually not allowed to share while annotating additional data in the new center is time-consuming and expensive. In this paper, we propose a Model Adaptive Tooth Segmentation (MATS) framework to alleviate these issues. Taking the trained model from a source center as input, MATS adapts it to different target centers without data transmission or additional annotations, as inspired by the source data-free domain adaptation (SFDA) paradigm. The model adaptation in MATS is realized by a tooth-level feature prototype learning module, a progressive pseudo-labeling module and a tooth-prior regularized information maximization loss. Experiments on a dataset with tooth abnormalities and a real-world cross-center dataset show that MATS can consistently surpass existing baselines. The effectiveness is further verified with extensive ablation studies and statistical analysis, demonstrating its applicability for privacy-preserving tooth segmentation in real-world digital dentistry. ",
        "authors": "Ruizhe Chen, Jianfei Yang, YANG FENG, Jin Hao, Zuozhu Liu",
        "award": "",
        "id": "48",
        "melba": "False",
        "or_id": "O2DerS5oQ1",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P048.pdf",
        "title": "Model Adaptive Tooth Segmentation",
        "yt_full": "https://youtu.be/bkF1lfFwWd0"
    },
    "P050": {
        "abstract": "In recent years, Transformer-based models have gained attention in the field of medical image segmentation, with research exploring ways to integrate them with established architectures such as Unet. However, the high computational demands of these models have led most current approaches to focus on segmenting 2D slices of MRI or CT images, which can limit the ability of the model to learn semantic information in the depth axis and result in output with uneven edges. Additionally, the small size of medical image datasets, particularly those for brain tumor segmentation, poses a challenge for training transformer models. To address these issues, we propose 3D Medical Axial Transformer (MAT), a lightweight, end-to-end model for 3D brain tumor segmentation that employs an axial attention mechanism to reduce computational demands and self-distillation to improve performance on small datasets. Results indicate that our approach, which has fewer parameters and a simpler structure than other models, achieves superior performance and produces clearer output boundaries, making it more suitable for clinical applications.",
        "authors": "Cheng Liu, Hisanor Kiryu",
        "award": "",
        "id": "50",
        "melba": "False",
        "or_id": "PX-jt92kQUM",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T17",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "3D Medical Axial Transformer: A Lightweight Transformer Model for 3D Brain Tumor Segmentation",
        "yt_full": ""
    },
    "P053": {
        "abstract": "We propose an image synthesis mechanism for multi-sequence prostate MR images conditioned on text, to control lesion presence and sequence, as well as to generate paired bi-parametric images conditioned on images e.g. for generating diffusion-weighted MR from T2-weighted MR for paired data, which are two challenging tasks in pathological image synthesis. Our proposed mechanism utilises and builds upon the recent stable diffusion model by proposing image-based conditioning for paired data generation. We validate our method using 2D image slices from real suspected prostate cancer patients. The realism of the synthesised images is validated by means of a blind expert evaluation for identifying real versus fake images, where a radiologist with 4 years experience reading urological MR only achieves 59.4\\% accuracy across all tested sequences (where chance is 50\\%). For the first time, we evaluate the realism of the generated pathology by blind expert identification of the presence of suspected lesions, where we find that the clinician performs similarly for both real and synthesised images, with a 2.9 percentage point difference in lesion identification accuracy between real and synthesised images, demonstrating the potentials in radiological training purposes. Furthermore, we also show that a machine learning model, trained for lesion identification, shows better performance (76.2\\% vs 70.4\\%, statistically significant improvement) when trained with real data augmented by synthesised data as opposed to training with only real images, demonstrating usefulness for model training.",
        "authors": "Shaheer U. Saeed, Tom Syer, Wen Yan, Qianye Yang, Mark Emberton, Shonit Punwani, Matthew John Clarkson, Dean Barratt, Yipeng Hu",
        "award": "",
        "id": "53",
        "melba": "False",
        "or_id": "3QnxUSzR7iu",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P053.pdf",
        "title": "Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion",
        "yt_full": "https://youtu.be/4lqgp8BKcfg"
    },
    "P054": {
        "abstract": "The image acquisition parameters (IAPs) used to create MRI scans are central to defining the appearance of the images. Deep learning models trained on data acquired using certain parameters might not generalize well to images acquired with different parameters. Being able to recover such parameters directly from an image could help determine whether a deep learning model is applicable, and could assist with data harmonization and/or domain adaptation. Here, we introduce a neural network model that can predict many complex IAPs used to generate an MR image with high accuracy solely using the image, with a single forward pass. These predicted parameters include field strength, echo and repetition times, acquisition matrix, scanner model, scan options, and others. Even challenging parameters such as contrast agent type can be predicted with good accuracy. We perform a variety of experiments and analyses of our model's ability to predict IAPs on many MRI scans of new patients, and demonstrate its usage in a realistic application. Predicting IAPs from the images is an important step toward better understanding the relationship between image appearance and IAPs. This in turn will advance the understanding of many concepts related to the generalizability of neural network models on medical images, including domain shift, domain adaptation, and data harmonization. ",
        "authors": "Nicholas Konz, Maciej A Mazurowski",
        "award": "",
        "id": "54",
        "melba": "False",
        "or_id": "JZBNeVLAqp",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W10",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images",
        "yt_full": ""
    },
    "P055": {
        "abstract": "Renal transplantation emerges as the most effective solution for end-stage renal disease. Occurring from complex causes, a substantial risk of transplant chronic dysfunction persists and may lead to graft loss. Medical imaging plays a substantial role in renal transplant monitoring in clinical practice. However, graft supervision is multi-disciplinary, notably joining nephrology, urology, and radiology, while identifying robust biomarkers from such high-dimensional and complex data for prognosis is challenging. In this work, taking inspiration from the recent success of Large Language Models (LLMs), we propose MEDIMP -- Medical Images and clinical Prompts -- a model to learn meaningful multi-modal representations of renal transplant Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE MRI) by incorporating structural clinicobiological data after translating them into text prompts. MEDIMP is based on contrastive learning from joint text-image paired embeddings to perform this challenging task. Moreover, we propose a framework that generates medical prompts using automatic textual data augmentations from LLMs. Our goal is to learn meaningful manifolds of renal transplant DCE MRI, interesting for the prognosis of the transplant or patient status (2, 3, and 4 years after the transplant), fully exploiting the limited available multi-modal data most efficiently. Extensive experiments and comparisons with other renal transplant representation learning methods with limited data prove the effectiveness of MEDIMP in a relevant clinical setting, giving new directions toward medical prompts. Our code is available at https://github.com/leomlck/MEDIMP.",
        "authors": "Leo Milecki, Vicky Kalogeiton, Sylvain Bodard, Dany Anglicheau, Jean-Michel Correas, Marc-Olivier Timsit, Maria Vakalopoulou",
        "award": "",
        "id": "55",
        "melba": "False",
        "or_id": "jt-ochRhqG",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W22",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\nWednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "/virtual/poster/P055.pdf",
        "title": "MEDIMP: 3D Medical Images and clinical Prompts for renal transplant representation learning",
        "yt_full": "https://youtu.be/PgGGqhpntTs"
    },
    "P062": {
        "abstract": "Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learning-based approaches focus on accurate registration of folding patterns of the cerebral cortex, and assume that geometry predicts function and thus functional areas will also be well aligned. However, structure/functional variability of anatomically corresponding areas across subjects has been widely reported. In this work, we introduce a learning-based cortical registration framework, JOSA, which jointly aligns folding patterns and functional maps while simultaneously learning an optimal atlas. We demonstrate that JOSA can substantially improve registration performance in both anatomical and functional domains over existing methods. By employing a semi-supervised training strategy, the proposed framework obviates the need for functional data during inference, enabling its use in broad neuroscientific domains where functional data may not be observed. The source code of JOSA will be released to the public at https://voxelmorph.net.",
        "authors": "Jian Li, Greta Tuckute, Evelina Fedorenko, Brian L Edlow, Bruce Fischl, Adrian V Dalca",
        "award": "",
        "id": "62",
        "melba": "False",
        "or_id": "n9v_BuIcY7G",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T18",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Joint cortical registration of geometry and function using semi-supervised learning",
        "yt_full": ""
    },
    "P067": {
        "abstract": "Recently, various deep learning methods have shown significant successes in medical image analysis, especially in the detection of cancer metastases in hematoxylin and eosin (H&E) stained whole-slide images (WSIs). However, in order to obtain good performance, these research achievements rely on hundreds of well-annotated WSIs. In this study, we tackle the tumor localization and detection problem under the setting of few labeled whole slide images and introduce a patch-based analysis pipeline based on the latest reverse knowledge distillation architecture. To address the extremely unbalanced normal and tumorous samples in training sample collection, we applied the focal loss formula to the representation similarity metric for model optimization. Compared with prior arts, our method achieves similar performance by less than ten percent of training samples on the public Camelyon16 dataset. In addition, this is the first work that show the great potential of the knowledge distillation models in computational histopathology. Our python implementation will be publically accessible upon paper acceptance.",
        "authors": "Yinsheng He, Xingyu Li",
        "award": "",
        "id": "67",
        "melba": "False",
        "or_id": "BF4NpwMuei",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W11",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data",
        "yt_full": ""
    },
    "P068": {
        "abstract": "Mild cognitive impairment (MCI), as a transitional state between normal cognition and Alzheimer's disease (AD), is crucial for taking preventive interventions in order to slow down AD progression. Given the high relevance of brain atrophy and the neurodegeneration process of AD, we propose a novel mesh-based pooling module, RegionPool, to investigate the morphological changes in brain shape regionally. We then present a geometric deep learning framework with the RegionPool and graph attention convolutions to perform binary classification on MCI subtypes (EMCI/LMCI). Our model does not require feature engineering and relies only on the relevant geometric information of T1-weighted magnetic resonance imaging (MRI) signals. Our evaluation reveals the state-of-the-art classification capabilities of our network and shows that current empirically derived MCI subtypes cannot identify heterogeneous patterns of cortical atrophy at the MCI stage. The class activation maps (CAMs) generated from the correct predictions provide additional visual evidence for our model's decisions and are consistent with the atrophy patterns reported by the relevant literature.",
        "authors": "Jiaqi Guo, Emanuel Azcona, Santiago Lopez-Tapia, Aggelos Katsaggelos",
        "award": "",
        "id": "68",
        "melba": "False",
        "or_id": "J4JWTCq14u",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M18",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Stage Detection of Mild Cognitive Impairment: Region-dependent Graph Representation Learning on Brain Morphable Meshes",
        "yt_full": ""
    },
    "P070": {
        "abstract": "While there have been many studies on using deep learning for medical image analysis, the lack of manually annotated data remains a challenge in training a deep learning model for segmentation of medical images. This work shows how the kaleidoscope transform (KT) can be applied to a 3D convolutional neural network to improve its generalizability when the training set is extremely small. In this study, the KT was applied to a context aggregation network (CAN) for semantic segmentation of anatomical structures in knee MR images. In the proposed model, KAN3D, the input image is rearranged into a batch of downsampled images (KT) before the convolution operations, and then the voxels are rearranged back to their original positions (inverse KT) after the convolution operations to produce the predicted segmentation mask for the input image. Compared to the CAN3D (without the KT), the KAN3D was able to reduce overfitting without data augmentation while maintaining a fast training and inference time. The paper discusses the observed advantages and disadvantages of KAN3D.",
        "authors": "Boyeong Woo, Marlon Bran Lorenzana, Craig Engstrom, William Baresic, Jurgen Fripp, Stuart Crozier, Shekhar S. Chandra",
        "award": "",
        "id": "70",
        "melba": "False",
        "or_id": "80ZHtBKHKHo",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P070.pdf",
        "title": "Semantic Segmentation of 3D Medical Images Through a Kaleidoscope: Data from the Osteoarthritis Initiative",
        "yt_full": "https://youtu.be/03tWDzQfWlg"
    },
    "P071": {
        "abstract": "High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, extracting representative features from high content images that can capture subtle nuances in phenotypes remains challenging. The lack of high-quality labels makes it difficult to achieve satisfactory results with supervised deep learning. Self-Supervised learning methods have shown great success on natural images, and offer an attractive alternative also to microscopy images. However, we find that self-supervised learning techniques underperform on high content imaging assays. One challenge is the undesirable domain shifts present in the data known as batch effects, which may be caused by biological noise or uncontrolled experimental conditions. To this end, we introduce Cross-Domain Consistency Learning (CDCL), a novel approach that is able to learn in the presence of batch effects. CDCL enforces the learning of biological similarities while disregarding undesirable batch-specific signals, which leads to more useful and versatile representations. These features are organised according to their morphological changes and are more useful for downstream tasks - such as distinguishing treatments and mechanism of action.",
        "authors": "Johan Fredin Haslum, Christos Matsoukas, Karl-Johan Leuchowius, Erik M\u00fcllers, Kevin Smith",
        "award": "",
        "id": "71",
        "melba": "False",
        "or_id": "PzzhiSNnyF8",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W35",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Metadata-guided Consistency Learning for High Content Images",
        "yt_full": ""
    },
    "P073": {
        "abstract": "Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks do not provide explanations for their predictions, which can lead to mistrust and suboptimal human-ML collaboration. Post-hoc explanation techniques, which are widely used in practice, have been shown to suffer from severe conceptual problems. Furthermore, as we show in this paper, current explanation techniques do not perform adequately in the multi-label scenario, in which multiple medical findings may co-occur in a single image. We propose Attri-Net, an inherently interpretable model for multi-label classification. Attri-Net is a powerful classifier that provides transparent, trustworthy, and human-understandable explanations. The model first generates class-specific attribution maps based on counterfactuals to identify which image regions correspond to certain medical findings. Then a simple logistic regression classifier is used to make predictions based solely on these attribution maps. We compare Attri-Net to five post-hoc explanation techniques and one inherently interpretable classifier on three chest X-ray datasets. We find that Attri-Net produces high-quality multi-label explanations consistent with clinical knowledge and has comparable classification performance to state-of-the-art classification models.",
        "authors": "Susu Sun, Stefano Woerner, Andreas Maier, Lisa M. Koch, Christian F. Baumgartner",
        "award": "",
        "id": "73",
        "melba": "False",
        "or_id": "OpmQOkfizzM",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M19",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "/virtual/poster/P073.pdf",
        "title": "Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals",
        "yt_full": "https://youtu.be/x44gbPrkWl8"
    },
    "P075": {
        "abstract": "Federated learning and its application to medical image segmentation have recently become a popular research topic. This training paradigm suffers from statistical heterogeneity between participating institutions\u2019 local datasets, incurring convergence slowdown as well as potential accuracy loss compared to classical training.  To mitigate this effect, federated personalization emerged as the federated optimization of one model per distribution. We propose a novel personalization algorithm tailored to the feature shift induced by the usage of different scanners and acquisition parameters by different institutions. This method is the first to account for both inter and intra-institution feature shifts (multiple scanners used in a single institution). It is based on the computation, within each centre, of a series of radiomic features capturing the global texture of each 3D image volume, followed by a clustering analysis pooling all feature vectors transferred from the local institutions to the central server. Each computed clustered decentralized dataset (potentially including data from different institutions) then serves to finetune a global model obtained through classical federated learning. We validate our approach on the Federated Brain Tumor Segmentation 2022 Challenge dataset (FeTS2022).",
        "authors": "Matthis Manthe, Stefan Duffner, Carole Lartizien",
        "award": "",
        "id": "75",
        "melba": "False",
        "or_id": "1CyXExO15K",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P075.pdf",
        "title": "Whole brain radiomics for clustered federated personalization in brain tumor segmentation",
        "yt_full": "https://youtu.be/NCbSwfMRPzg"
    },
    "P076": {
        "abstract": "Automated generation of clinically accurate radiology reports can improve patient care. Previous report generation methods that rely on image captioning models often generate incoherent and incorrect text due to their lack of relevant domain knowledge, while retrieval-based attempts frequently retrieve reports that are irrelevant to the input image. In this work, we propose Contrastive X-Ray REport Match (X-REM), a novel retrieval-based radiology report generation module that uses an image-text matching score to measure the similarity of a chest X-ray image and radiology report for report retrieval. We observe that computing the image-text matching score with a language-image model can effectively capture the fine-grained interaction between image and text that is often lost when using cosine similarity. X-REM outperforms multiple prior radiology report generation modules in terms of both natural language and clinical metrics. Human evaluation of the generated reports suggests that X-REM increased the number of zero-error reports and decreased the average error severity compared to the baseline retrieval approach. Our code is available at: https://github.com/rajpurkarlab/X-REM",
        "authors": "Jaehwan Jeong, Katherine Tian, Andrew Li, Sina Hartung, Subathra Adithan, Fardad Behzadi, Juan Calle, David Osayande, Michael Pohlen, Pranav Rajpurkar",
        "award": "",
        "id": "76",
        "melba": "False",
        "or_id": "aZ0OuYMSMMZ",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P076.pdf",
        "title": "Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation",
        "yt_full": "https://youtu.be/jqlCFoU-w2U"
    },
    "P078": {
        "abstract": "This work proposes MultiTask Learning for accelerated-MRI Reconstruction and Segmentation (MTLRS). Unlike the common single-task approaches, MultiTask Learning identifies relations between multiple tasks to improve the performance of all tasks. The proposed MTLRS consists of a unique cascading architecture, where a recurrent reconstruction network and a segmentation network inform each other through hidden states. The features of the two networks are shared and implicitly enforced as inductive bias. To evaluate the benefit of MTLRS, we compare performing the two tasks of accelerated-MRI reconstruction and MRI segmentation with pre-trained, sequential, end-to-end, and joint approaches. A synthetic multicoil dataset is used to train, validate, and test all approaches with five-fold cross-validation. The dataset consists of 3D FLAIR brain data of relapsing-remitting Multiple Sclerosis patients with known white matter lesions. The acquisition is prospectively undersampled by approximately 7.5 times compared to clinical standards. Reconstruction performance is evaluated by Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR). Segmentation performance is evaluated by Dice score for combined brain tissue and white matter lesion segmentation and by per lesion Dice score. Results show that MTLRS outperforms other evaluated approaches, providing high-quality reconstructions and accurate white matter lesion segmentation. A significant correlation was found between the performance of both tasks (SSIM and per lesion Dice score, $\\rho=0.92$, $p=0.0005$). Our proposed MTLRS demonstrates that accelerated-MRI reconstruction and MRI segmentation can be effectively combined to improve performance on both tasks, potentially benefiting clinical settings.",
        "authors": "Dimitrios Karkalousos, Ivana Isgum, Henk Marquering, Matthan W. A. Caan",
        "award": "",
        "id": "78",
        "melba": "False",
        "or_id": "ci2Fg31H0T",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M20",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "MultiTask Learning for accelerated-MRI Reconstruction and Segmentation of Brain Lesions in Multiple Sclerosis",
        "yt_full": ""
    },
    "P082": {
        "abstract": "Recently, the study of multi-modal brain networks has dramatically facilitated the efficiency in brain disorder diagnosis by characterizing multiple types of connectivity of brain networks and their intrinsic complementary information. Despite the promising performance achieved by multi-modal technologies, most existing multi-modal approaches can only learn from samples with complete modalities, which wastes a considerable amount of mono-modal data. Otherwise, most existing data imputation approaches still rely on a large number of samples with complete modalities. In this study, we propose a modal-mixup data imputation method by randomly sampling incomplete samples and synthesizing them into complete data for auxiliary training. Moreover, to mitigate the noise in the complementary information between unpaired modalities in the synthesized data, we introduce a bilateral network with deep supervision for improving and regularizing mono-modal representations with disease-specific information. Experiments on the ADNI dataset demonstrate the superiority of our proposed method for disease classification in terms of different rates of samples with complete modalities.",
        "authors": "Yanwu Yang, Hairui Chen, Zhikai Chang, Yang Xiang, Chenfei Ye, Ting Ma",
        "award": "",
        "id": "82",
        "melba": "False",
        "or_id": "WjrcYNTPunQ",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P082.pdf",
        "title": "Incomplete learning of multi-modal connectome for brain disorder diagnosis via modal-mixup and deep supervision",
        "yt_full": "https://youtu.be/4eLh_bwT_Tk"
    },
    "P083": {
        "abstract": "The use of supervised deep learning techniques to detect pathologies in brain MRI scans can be challenging due to the diversity of brain anatomy and the need for annotated data sets. An alternative approach is to use unsupervised anomaly detection, which only requires sample-level labels of healthy brains to create a reference representation. This reference representation can then be compared to unhealthy brain anatomy in a pixel-wise manner to identify abnormalities. To accomplish this, generative models are needed to create anatomically consistent MRI scans of healthy brains. While recent diffusion models have shown promise in this task, accurately generating the complex structure of the human brain remains a challenge. In this paper, we propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy, using spatial context to guide and improve reconstruction. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.",
        "authors": "Finn Behrendt, Debayan Bhattacharya, Julia Kr\u00fcger, Roland Opfer, Alexander Schlaefer",
        "award": "",
        "id": "83",
        "melba": "False",
        "or_id": "O-uZr5S1tJE",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W13",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P083.pdf",
        "title": "Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI",
        "yt_full": "https://youtu.be/tiS7ciIOG3I"
    },
    "P085": {
        "abstract": "Dynamic Magnetic Resonance Imaging (MRI) is known to be a powerful and reliable technique for the dynamic imaging of internal organs and tissues, making it a leading diagnostic tool. A major difficulty in using MRI in this setting is the relatively long acquisition time (and, hence, increased cost) required for imaging in high spatio-temporal resolution, leading to the appearance of related motion artifacts and decrease in resolution. Compressed Sensing (CS) techniques have become a common tool to reduce MRI acquisition time by subsampling images in the $k$-space according to some acquisition trajectory. Several studies have particularly focused on applying deep learning techniques to learn these acquisition trajectories in order to attain better image reconstruction, rather than using some predefined set of trajectories. To the best of our knowledge, learning acquisition trajectories has been only explored in the context of static MRI. In this study, we consider acquisition trajectory learning in the dynamic imaging setting. We design an end-to-end pipeline for the joint optimization of multiple per-frame acquisition trajectories along with a reconstruction neural network, and demonstrate improved image reconstruction quality in shorter acquisition times. The code for reproducing all experiments will accompany the paper.",
        "authors": "Tamir Shor, Tomer Weiss, Dor Noti, Alex Bronstein",
        "award": "",
        "id": "85",
        "melba": "False",
        "or_id": "NXPLq6w___",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M21",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Multi PILOT: Feasible Learned Multiple Acquisition Trajectories For Dynamic MRI",
        "yt_full": ""
    },
    "P087": {
        "abstract": "Preparing data for machine learning tasks in health and life science applications requires decisions that affect the cost, model properties and performance. In this work, we study the implication of data collection strategies, focusing on a case study of mitosis detection. Specifically, we investigate the use of expert and crowd-sourced labelers, the impact of aggregated vs single labels, and the framing of the problem as either classification or object detection. Our results demonstrate the value of crowd-sourced labels, importance of uncertainty quantification, and utility of negative samples. ",
        "authors": "Zongliang Ji, Philip Rosenfield, Christina Eng, Sarah Bettigole, Danielle C Gibson, Hamid Masoudi, Matthew Hanna, Nicolo Fusi, Kristen A Severson",
        "award": "",
        "id": "87",
        "melba": "False",
        "or_id": "RyV4J_PjNv",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T19",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Considerations for data acquisition and modeling strategies: Mitosis detection in computational pathology",
        "yt_full": ""
    },
    "P090": {
        "abstract": "Graph neural networks (GNNs) have been proposed for medical image segmentation, by predicting anatomical structures represented by graphs of vertices and edges. One such type of graphs are predefined with fixed size and connectivity to represent a reference of anatomical regions of interest, thus known as templates. This work explores the potentials in these GNNs with common topology for establishing spatial correspondence, implicitly maintained during segmenting two or more images. With an example application of registering local vertebral sub-regions found in CT images, our experimental results showed that the GNN-based segmentation is capable of accurate and reliable localisation of the same interventionally interesting structures between images, not limited to the segmentation classes. The reported average target registration errors of 2.2$\\pm$1.3 mm and 2.7$\\pm$1.4 mm, for aligning holdout test images with a reference and for aligning two test images, respectively, were by a considerable margin lower than those from the tested non-learning and learning-based registration algorithms. Further ablation studies assess the contributions towards the registration performance, from individual components in the originally segmentation-purposed network and its training algorithm. The results highlight that the proposed segmentation-in-lieu-of-registration approach shares methodological similarity with existing registration methods, such as the use of displacement smoothness constraint and point distance minimisation albeit on non-grid graphs, which interestingly yielded benefits for both segmentation and registration. We therefore conclude that the template-based GNN segmentation can effectively establish spatial correspondence in our application, without any other dedicated registration algorithms.",
        "authors": "Qian Li, Yunguan Fu, Qianye Yang, Zhijiang Du, Hongjian Yu, Yipeng Hu",
        "award": "",
        "id": "90",
        "melba": "False",
        "or_id": "d7J0IiMqcZd",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P090.pdf",
        "title": "Spatial Correspondence between Graph Neural Network-Segmented Images",
        "yt_full": "https://youtu.be/Sh0d9CWp9dE"
    },
    "P101": {
        "abstract": "We propose a few shot learning approach for the problem of hematopoietic cell classification in digital pathology. In hematopoiesis cell classification, the classes correspond to the different stages of the cellular maturation process. Two  consecutive stage categories are considered to have a neighborhood relationship, which implies a visual similarity between the two categories. We propose RelationVAE which incorporates these relationships between hematopoietic cell classes to robustly generate more data for the classes with limited training data. Specifically, we first model these relationships using a graphical model, and propose RelationVAE, a deep generative model which implements the graphical model. RelationVAE is trained to optimize the lower bound of the pairwise data likelihood of the graphical model. In this way, it can identify class level features of a specific class from a small number of input images together with the knowledge transferred from visually similar classes, leading to more robust sample synthesis. The experiments on our collected hematopoietic dataset show the improved results of our proposed RelationVAE over a baseline VAE model and other few shot learning methods.",
        "authors": "Vu Nguyen, Prantik Howlader, Le Hou, Dimitris Samaras, Rajarsi R. Gupta, Joel Saltz",
        "award": "",
        "id": "101",
        "melba": "False",
        "or_id": "9yTSJIb5t_Z",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M22",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Few Shot Hematopoietic Cell Classification",
        "yt_full": ""
    },
    "P105": {
        "abstract": "We propose the patient-specific Liver Mesh Recovery (LMR) framework, to automatically achieve Augmented Reality (AR) guidance by registering a preoperative 3D model in Minimally Invasive Liver Resection (MILR). Existing methods solve registration in MILR by pose estimation followed with numerical optimisation and suffer from a prohibitive intraoperative runtime. The proposed LMR is inspired by the recent Human Mesh Recovery (HMR) framework and forms the first learning-based method to solve registration in MILR. In contrast to existing methods, the computation load in LMR occurs preoperatively, at training time. We construct a patient-specific deformation model and generate patient-specific training data reproducing the typical defects of the automatically detected registration primitives. Experimental results show that LMR's registration accuracy is on par with optimisation-based methods, whilst running in real-time intraoperatively.",
        "authors": "Mathieu Labrunie, Daniel Pizarro, Christophe Tilmant, Adrien Bartoli",
        "award": "",
        "id": "105",
        "melba": "False",
        "or_id": "nG87JqzSMc",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T20",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Automatic 3D/2D Deformable Registration in Minimally Invasive Liver Resection using a Mesh Recovery Network",
        "yt_full": ""
    },
    "P110": {
        "abstract": "Diffusion MRI (dMRI) streamline tractography is the gold-standard for in vivo estimation of white matter (WM) pathways in the brain. However, the high angular resolution dMRI acquisitions capable of fitting the microstructural models needed for tractography are often time-consuming and not routinely collected clinically, restricting the scope of tractography analyses. To address this limitation, we build on recent advances in deep learning which have demonstrated that streamline propagation can be learned from dMRI directly without traditional model fitting. Specifically, we propose learning the streamline propagator from T1w MRI to facilitate arbitrary tractography analyses when dMRI is unavailable. To do so, we present a novel convolutional-recurrent neural network (CoRNN) trained in a teacher-student framework that leverages T1w MRI, associated anatomical context, and streamline memory from data acquired for the Human Connectome Project. We characterize our approach under two common tractography paradigms, WM bundle analysis and structural connectomics, and find approximately a 5-15% difference between measures computed from streamlines generated with our approach and those generated using traditional dMRI tractography. When placed in the literature, these results suggest that the accuracy of WM measures computed from T1w MRI with our method is on the level of scan-rescan dMRI variability and raise an important question: is tractography truly a microstructural phenomenon, or has dMRI merely facilitated its discovery and implementation?",
        "authors": "Leon Yichen Cai, Ho Hin Lee, Nancy Rose Newlin, Cailey Irene Kerley, Praitayini Kanakaraj, Qi Yang, Graham Walter Johnson, Daniel Moyer, Kurt Gregory Schilling, Francois Rheault, Bennett A. Landman",
        "award": "",
        "id": "110",
        "melba": "False",
        "or_id": "TelM2TBYgoA",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W14",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context",
        "yt_full": ""
    },
    "P115": {
        "abstract": "Human brain tumours and more specifically gliomas are amongst the most life-threatening cancers which usually arise from abnormal growth of the glial stem cells. In practice, Magnetic Resonance Imaging (MRI) modalities, which offer different contrasts to elucidate tissue properties, provide comprehensive information regarding the brain\u2019s structure and also potential clues for detecting tumors. Hence, multi-modal MRI is commonly utilized for the diagnosis of brain tumors. However, since the set of acquired modalities may vary between clinical sites, brain tumor studies may miss one or two MRI modalities. To address missing information in an end-to-end manner, we propose MMCFormer, a novel missing modality compensation network. Our strategy builds upon 3D efficient transformer blocks and uses a co-training strategy to effectively train a missing modality network. To ensure feature consistency in a multi-scale fashion, MMCFormer utilizes global contextual agreement modules in each scale of the encoders. Furthermore, to transfer modality-specific representations, we propose to incorporate auxiliary tokens in the bottleneck stage to model interaction between full and missing-modality paths. On top of that, we include feature consistency losses to reduce the domain gap in network prediction and increase the prediction reliability for the missing modality path. Extensive experiments on the BraTS 2018 dataset demonstrate the benefits of our approach compared to competing approaches. The implementation code will be publicly available after the paper acceptance.",
        "authors": "Sanaz Karimijafarbigloo, Reza Azad, Amirhossein Kazerouni, Saeed Ebadollahi, Dorit Merhof",
        "award": "",
        "id": "115",
        "melba": "False",
        "or_id": "PD0ASSmvlE",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P115.pdf",
        "title": "MMCFormer: Missing Modality Compensation Transformer for Brain Tumor Segmentation",
        "yt_full": "https://youtu.be/_V7RsdtVSpo"
    },
    "P117": {
        "abstract": "Accurate detection of 3D landmarks is critical for evaluating and characterizing anatomical features and performing preoperative diagnostic screening. However, detecting 3D landmarks can be challenging due to the local structural homogeneity of medical images. To address this issue, physicians often annotate multiple landmarks in a single slice, particularly when estimating 3D distance or volume. In this study, we present a prior guided coarse-to-fine framework for efficient and accurate 3D medical landmark detection; we make use of the prior information that in specific settings physicians annotate multiple landmarks on a same slice. The coarse stage uses coordinate regression on downsampled 3D images to maintain the structural relationships across different landmarks. The fine stage categorizes landmarks as correlated and independent landmarks based on their annotation prior. For independent landmarks, we train multiple models to capture local features and ensure reliable local predictions. For correlated landmarks, we mimic the manual annotation process and propose a correlated landmark detection model that merges information from various patches to query key slices and identify correlated landmarks. Our method is extensively evaluated on two datasets, exhibiting superior performance with an average detection error of respective 3.29 mm and 2.13 mm.",
        "authors": "yijie pang, Pujin Cheng, Junyan Lyu, FAN lin, Xiaoying Tang",
        "award": "",
        "id": "117",
        "melba": "False",
        "or_id": "Ae1KAltzEd",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P117.pdf",
        "title": "Prior Guided 3D Medical Image Landmark Localization",
        "yt_full": "https://youtu.be/NhIr89da0bc"
    },
    "P118": {
        "abstract": "Variability in staining protocols, such as different slide preparation techniques, chemicals, and scanner configurations, can result in a diverse set of whole slide images (WSIs). This distribution shift can negatively impact the performance of deep learning models on unseen samples, presenting a significant challenge for developing new computational pathology applications. In this study, we propose a method for improving the generalizability of convolutional neural networks (CNNs) to stain changes in a single-source setting for semantic segmentation. Recent studies indicate that style features mainly exist as covariances in earlier network layers. We design a channel attention mechanism based on these findings that detects stain-specific features and modify the previously proposed stain-invariant training scheme. We reweigh the outputs of earlier layers and pass them to the stain-adversarial training branch. We evaluate our method on multi-center, multi-stain datasets and demonstrate its effectiveness through interpretability analysis. Our approach achieves substantial improvements over baselines and competitive performance compared to other methods, as measured by various evaluation metrics. We also show that combining our method with stain augmentation leads to mutually beneficial results and outperforms other techniques. Overall, our study makes significant contributions to the field of computational pathology.",
        "authors": "Kudaibergen Abutalip, Numan Saeed, Mustaqeem Khan, Abdulmotaleb El Saddik",
        "award": "",
        "id": "118",
        "melba": "False",
        "or_id": "uZ1SVZgEJ02",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T21",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Improving Stain Invariance of CNNs for Segmentation by Fusing Channel Attention and Domain-Adversarial Training",
        "yt_full": ""
    },
    "P121": {
        "abstract": "Deep learning-based models are widely used for disease classification in chest radiographs. This exam can be performed in one of two projections (posteroanterior or anteroposterior), depending on the direction that the X-ray beam travels through the body. Since projection visibly affects the way anatomical structures appear in the scans, it may introduce bias in classifiers, especially when spurious correlations between a given disease and a projection occur. This paper examines the influence of chest radiograph projection on the performance of deep learning-based classification models and proposes an approach to mitigate projection-induced bias. Results show that a DenseNet-121 model is better at classifying images from the most representative projection in the data set, suggesting that projection is taken into account by the classifier. Moreover, this model can classify chest X-ray projection better than any of the fourteen radiological findings considered, without being explicitly trained for that task, putting it at high risk for projection bias. We propose a label-conditional gradient reversal framework to make the model insensitive to projection, by forcing the extracted features to be simultaneously good for disease classification and bad for projection classification, resulting in a framework with reduced projection-induced bias.",
        "authors": "Sofia Cardoso Pereira, Joana Rocha, Alex Gaudio, Asim Smailagic, Aur\u00e9lio Campilho, Ana Maria Mendon\u00e7a",
        "award": "",
        "id": "121",
        "melba": "False",
        "or_id": "k8K2zEiv_m",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W15",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Addressing Chest Radiograph Projection Bias in Deep Classification Models",
        "yt_full": ""
    },
    "P126": {
        "abstract": "Miscalibration of deep neural networks (DNNs) can lead to unreliable predictions and hinder their use in clinical decision-making. This miscalibration is often caused by overconfident probability estimates. Calibration techniques such as model ensembles, regularization terms, and post-hoc scaling of the predictions can be employed to improve the calibration performance of DNNs. In contrast to DNNs, graph neural networks (GNNs) tend to exhibit underconfidence. In this study, we investigate the efficacy of calibration techniques developed for DNNs when applied to GNNs trained on medical image data, and compare the calibration performance of binary and multiclass node classification on a benchmark dataset and a medical image dataset. We find that post-hoc methods using Platt scaling or Temperature scaling, or methods that add a regularization term to the loss function during training are most effective to improve calibration. Our results further indicate that these calibration techniques are more effective for multiclass classification tasks compared to binary classification tasks.",
        "authors": "Iris Vos, Ishaan Bhat, Birgitta Velthuis, Ynte Ruigrok, Hugo Kuijf",
        "award": "",
        "id": "126",
        "melba": "False",
        "or_id": "xY3agyj5Eq",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M23",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Calibration techniques for node classification using graph neural networks on medical image data",
        "yt_full": ""
    },
    "P128": {
        "abstract": "Computer-aided diagnosis systems for automatic prostate cancer diagnosis can provide radiologists with decision support during image reading. However, in this case, patient-relevant information often remains unexploited due to the greater focus on the image recognition side, with various imaging devices and modalities, while omitting other potentially valuable clinical data. Therefore, our work investigates the performance of recent methods for the fusion of rich image data and heterogeneous tabular data. Those data may include patient demographics as well as laboratory data, e.g., prostate-specific antigen (PSA). Experiments on the large dataset (3800 subjects) indicated that when using the fusion method with demographic data in clinically significant prostate cancer (csPCa) detection tasks, the mean area under the receiver operating characteristic curve (ROC AUC) has improved significantly from 0.736 to 0.765. We also observed that the na\u00efve concatenation performs similarly or even better than the \\mbox{state-of-the-art} fusion modules. We also achieved better prediction quality in grading prostate disease by including more samples from longitudinal PSA profiles in the tabular feature set. Thus, by including the three last PSA samples per patient, the best-performing model has reached AUC of 0.794 and a quadratic weighted kappa score (QWK) of 0.464, which constitutes a significant improvement compared with the image-only method, with ROC AUC of 0.736 and QWK of 0.342.",
        "authors": "Oleksii Bashkanov, Marko Rak, Lucas Engelage, Christianus Hansen",
        "award": "",
        "id": "128",
        "melba": "False",
        "or_id": "Oji7tGC4oI",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T22",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Automatic Patient-level Diagnosis of Prostate Disease with Fused 3D MRI and Tabular Clinical Data",
        "yt_full": ""
    },
    "P129": {
        "abstract": "Chest X-ray images are often used to determine the proper placement of catheters, as incorrect placement can lead to severe complications. With the advent of deep learning, computer-aided detection methods have been developed to assist radiologists in identifying catheter misplacement by detecting and highlighting the catheter's path. However, obtaining large, pixel-wise labeled datasets can be challenging due to the labor-intensive nature of annotation. To address this issue, we proposed a novel semi-supervised learning method that combines contrastive loss and topology loss. This method takes advantage of the known topological properties of catheters and does not require extensive labeling. We collected 7,378 chest X-ray images from the *****, which were labeled for misplacement of nasogastric and endotracheal tube catheters, and included pixel-wise annotation. Moreover, the CLiP dataset was used as an unlabeled dataset for semi-supervised learning. We used a hybrid U-Net architecture with an added classification head to perform simultaneous segmentation of the catheter and misplacement classification. Our model achieved an average AUC of 0.977 for classification and a average Dice score of 0.598 for segmentation.",
        "authors": "Tianyu Hwang, Chih-Hung Wang, Holger R Roth, Dong Yang, Can Zhao, Chien-Hua Huang, Weichung Wang",
        "award": "",
        "id": "129",
        "melba": "False",
        "or_id": "9mPSPWo5tzu",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P129.pdf",
        "title": "Semi-supervised Learning with Contrastive and Topology Losses for Catheter Segmentation and Misplacement Prediction",
        "yt_full": "https://youtu.be/pLRFWXEZqI0"
    },
    "P132": {
        "abstract": "Surgical robotics holds much promise for improving patient safety and clinician experience in the Operating Room (OR). However, it also comes with new challenges, requiring strong team coordination and effective OR management. Automatic detection of surgical activities is a key requirement for developing AI-based intelligent tools to tackle these challenges. The current state-of-the-art surgical activity recognition methods however operate on image-based representations and depend on large-scale labeled datasets whose collection is time-consuming and resource-expensive. This work proposes a new sample-efficient and object-based approach for surgical activity recognition in the OR. Our method focuses on the geometric arrangements between clinicians and surgical devices, thus utilizing the significant object interaction dynamics in the OR. We conduct experiments in a low-data regime study for long video activity recognition. We also benchmark our method against other object-centric approaches on clip-level action classification and show superior performance.",
        "authors": "Idris Hamoud, Muhammad Abdullah Jamal, Vinkle Srivastav, Didier MUTTER, Nicolas Padoy, Omid Mohareri",
        "award": "",
        "id": "132",
        "melba": "False",
        "or_id": "C6EJi0SS7j",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M24",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "ST(OR)$^2$: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room",
        "yt_full": ""
    },
    "P133": {
        "abstract": "Performance of deep learning algorithms varies due to their development data and training method, but also due to several stochastic processes during training. Due to these random factors, a single training run may not accurately reflect the performance of a given training method. Statistical comparisons in literature between different deep learning training methods typically ignore this performance variation between training runs and incorrectly claim significance of changes in training method. We hypothesize that the impact of such performance variation is substantial, such that it may invalidate biomedical competition leaderboards and some scientific papers. To test this, we investigate the reproducibility of training deep learning algorithms for medical image analysis. We repeated training runs from prior scientific studies: three diagnostic tasks (pancreatic cancer detection in CT, clinically significant prostate cancer detection in MRI, and lung nodule malignancy risk estimation in low-dose CT) and two organ segmentation tasks (pancreas segmentation in CT and prostate segmentation in MRI). A previously published top-performing algorithm for each task was trained multiple times to determine the variance in model performance. For all three diagnostic algorithms, performance variation from retraining was significant compared to data variance. Statistically comparing independently trained algorithms from the same training method using the same dataset should follow the null hypothesis, but we observed claimed significance with a p-value below 0.05 in $15\\%$ of comparisons with conventional testing (paired bootstrapping). We conclude that variance in model performance due to retraining is substantial and should be accounted for. ",
        "authors": "Joeran Bosma, Dre Peeters, Nat\u00e1lia Alves, Anindo Saha, Zaigham Saghir, Colin Jacobs, henkjan huisman",
        "award": "",
        "id": "133",
        "melba": "False",
        "or_id": "MR01DcGST9",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T23",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Reproducibility of Training Deep Learning Models for Medical Image Analysis",
        "yt_full": ""
    },
    "P136": {
        "abstract": "The image quality of coronary CT angiography (CCTA) is important for the correct diagnosis of patients with suspected coronary artery disease, which is heavily influenced by image acquisition. Timing of the contrast media injection specifically influences the level of arterial enhancement, and it is aimed to allow optimal assessment of the coronary artery morphology. However, a consensus on an optimal acquisition protocol that can account for the large variety in patient cohorts has not been reached, commonly resulting in suboptimal arterial enhancement. In this work, we propose a generative adversarial network for the retrospective correction of contrast media attenuation in CCTA, thus reducing the dependency on an optimal timing protocol at acquisition. We develop and evaluate the method in a set of 1,179 CCTA scans with varying levels of contrast enhancement. We evaluate the consistency of intensity values in the coronary arteries and evaluate performance of coronary centerline extraction as a commonly performed analysis task. Results show that correction of contrast media attenuation values in CCTA scans is feasible, and that it improves the performance of automatic centerline extraction. The method may allow improved analysis of coronary arteries  in CCTA scans with suboptimal contrast enhancement.",
        "authors": "Rudolf Leonardus Mirjam Van Herten, Louis van Harten, Nils Planken, Ivana Isgum",
        "award": "",
        "id": "136",
        "melba": "False",
        "or_id": "MIHF_buWGUQ",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W59",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Generative Adversarial Networks for Coronary CT Angiography Acquisition Protocol Correction with Explicit Attenuation Constraints",
        "yt_full": ""
    },
    "P137": {
        "abstract": "Deep learning has led to many advances in medical image analysis for various clinical problems. However, most deep learning models are known to be sensitive to differences in the training and test data distributions, which can lead to a decrease in accuracy when applied in real-life scenarios. Thus far, various techniques have been developed to tackle this problem, primarily focusing on harmonizing feature representations from different datasets. Due to the recent increased interest in causal approaches in deep learning, explainable harmonization techniques have gained momentum lately but have not been applied broadly yet. Our study proposes a causal flow-based technique to overcome the problem of different feature distributions in multi-site data used for Parkinson's disease (PD) classification. Feature distributions from six different sites, with a total of 415 subjects (PD: 263, healthy controls: 152), were used for the experiments. A counterfactual approach to answer the question, ``How would brain MRI features appear if they were obtained at a different site?\" was developed using a causal normalizing flow. When tested on features from a previously unseen site, the counterfactual-based classifier demonstrated superior performance (weighted f1 = 0.68) compared to a classifier trained on purely observational data (weighted f1 = 0.36) and improved accuracy compared to a harmonization technique typically used in neurological settings (weighted f1 = 0.5). These results show that the proposed technique can effectively correct differences in multi-site feature distributions to facilitate generalizable deep-learning models.",
        "authors": "Vibujithan Vigneshwaran, Matthias Wilms, Milton Ivan Camacho Camacho, Raissa Souza, Nils Forkert",
        "award": "",
        "id": "137",
        "melba": "False",
        "or_id": "l4LDtGb8zL",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M25",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Improved multi-site Parkinson's disease classification using neuroimaging data with counterfactual inference",
        "yt_full": ""
    },
    "P138": {
        "abstract": "Recently, graph neural networks (GNNs) have shown success at learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data. The majority of existing GNN methods, however, assume brain graphs are static over time and the graph adjacency matrix is known prior to model training. These assumptions are at odds with neuroscientific evidence that brain graphs are time-varying with a connectivity structure that depends on the choice of functional connectivity measure. Noisy brain graphs that do not truly represent the underling fMRI data can have a detrimental impact on the performance of GNNs. As a solution, we propose Dynamic Brain Graph Structure Learning (DBGSL), a novel method for learning the optimal time-varying dependency structure of fMRI data induced by a downstream prediction task. Experiments demonstrate DBGSL achieves state-of-the-art performance for sex classification using real-world resting-state and task fMRI data. Moreover, analysis of the learnt dynamic graphs highlights prediction-related brain regions which align with existing neuroscience literature.",
        "authors": "Alexander Campbell, Antonio Giuliano Zippo, Luca Passamonti, Nicola Toschi, Pietro Lio",
        "award": "",
        "id": "138",
        "melba": "False",
        "or_id": "Us31horKNLG",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P138.pdf",
        "title": "DBGSL: Dynamic Brain Graph Structure Learning",
        "yt_full": ""
    },
    "P139": {
        "abstract": "Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. In this paper, we propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings. Specifically, DBGDGM represents brain graph nodes as embeddings sampled from a distribution over communities that evolve over time. We parameterise this community distribution using neural networks that learn from subject and node embeddings as well as past community assignments. Experiments demonstrate DBGDGM outperforms baselines in graph generation, dynamic link prediction, and is comparable for graph classification. Finally, an analysis of the learnt community distributions reveals overlap with known FCNs reported in neuroscience literature.",
        "authors": "Alexander Campbell, Simeon Emilov Spasov, Nicola Toschi, Pietro Lio",
        "award": "",
        "id": "139",
        "melba": "False",
        "or_id": "WHS3Zv9pxz",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P139.pdf",
        "title": "DBGDGM: Dynamic Brain Graph Deep Generative Model",
        "yt_full": ""
    },
    "P143": {
        "abstract": "Colonoscopy is considered the most effective screening test to detect colorectal cancer (CRC) and its precursor lesions, i.e., polyps. However, the procedure experiences high miss rates due to polyp heterogeneity and inter-observer dependency. Hence, several deep learning powered systems have been proposed considering the criticality of polyp detection and segmentation in clinical practices. Despite achieving improved outcomes, the existing automated approaches are inefficient in attaining real-time processing speed. Moreover, they suffer from a significant performance drop when evaluated on inter-patient data, especially those collected from different centers. Therefore, we intend to develop a novel real-time deep learning based architecture, Transformer based Residual network (TransNetR), for colon polyp segmentation and evaluate its diagnostic performance. The proposed architecture, TransNetR, is an encoder-decoder network that consists of a pre-trained ResNet50 as the encoder, three decoder blocks, and an upsampling layer at the end of the network. TransNetR obtains a high dice coefficient of 0.8706 and a mean Intersection over union of 0.8016 and retains a real-time processing speed of 54.60 on the Kvasir-SEG dataset. Apart from this,  the major contribution of the work lies in exploring the generalizability of the TransNetR by testing the proposed algorithm on the out-of-distribution (test distribution is unknown and different from training distribution) dataset. As a use case, we tested our proposed algorithm on the PolypGen (6 unique centers) dataset and two other popular polyp segmentation benchmarking datasets. We obtained state-of-the-art performance on all three datasets during out-of-distribution testing. The source code of TransNetR is available at https://github.com/Anonymous/TransNetR. ",
        "authors": "Debesh Jha, Nikhil Kumar Tomar, Vanshali Sharma, Ulas Bagci",
        "award": "",
        "id": "143",
        "melba": "False",
        "or_id": "-8mexJCWH_-",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W17",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing",
        "yt_full": ""
    },
    "P145": {
        "abstract": "Although human\u2019s ability to visually understand the structure of the World plays a crucial role in perceiving the World and making appropriate decisions, human perception does not solely rely on vision but amalgamates the information from acoustic, verbal, and visual stimuli. An active area of research has been revolving around designing an efficient framework that adapts to multiple modalities and ideally improves the performance of existing tasks. While numerous frameworks have proved effective on natural datasets like ImageNet, a limited number of studies have been carried out in the biomedical domain. In this work, we extend the available frameworks for natural data to biomedical data by leveraging the abundant, unstructured multi-modal data available as radiology images and reports. We attempt to answer the question, \u201dFor multi-modal learning, self-supervised learning and joint learning using both learning strategies, which one improves the visual representation for downstream chest radiographs classification tasks the most?\u201d. Our experiments indicated that in limited labeled data settings with 1% and 10% labeled data, the joint learning with multi-modal and self-supervised models outperforms self-supervised learning and is at par with multi-modal learning. Additionally, we found that multi-modal learning is generally more robust on out-of-distribution datasets. ",
        "authors": "Haoxu Huang, Samyak Rawlekar, Sumit Chopra, Cem M Deniz",
        "award": "",
        "id": "145",
        "melba": "False",
        "or_id": "S9EfOVFJIxQh",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M26",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Radiology Reports Improve Visual Representations Learned from Radiographs",
        "yt_full": ""
    },
    "P146": {
        "abstract": "Multiplex immunofluorescence (MxIF) is an advanced molecular imaging technique that can simultaneously provide biologists with multiple (i.e., more than 20) molecular markers on a single histological tissue section. Unfortunately, due to imaging restrictions, the more routinely used hematoxylin and eosin (H&E) stain is typically unavailable with MxIF on the same tissue section. As biological H&E staining is not feasible, previous efforts have been made to obtain H&E whole slide image (WSI) from MxIF via deep learning empowered virtual staining. However, the tiling effect is a long-lasting problem in high-resolution WSI-wise synthesis. The MxIF to H&E synthesis is no exception. Limited by computational resources, the cross-stain image synthesis is typically performed at the patch-level. Thus, discontinuous intensities might be visually identified along with the patch boundaries assembling all individual patches back to a WSI. In this work, we propose a deep learning based unpaired high-resolution image synthesis method to obtain virtual H&E WSIs from MxIF WSIs (each with 27 markers/stains) with reduced tiling effects. Briefly, we first extend the CycleGAN framework by adding simultaneous nuclei and mucin segmentation supervision as spatial constraints. Then, we introduce a random sliding window shifting strategy during the optimized inference stage to alleviate the tiling effects. The validation results show that our spatially constrained synthesis method achieves a 56% performance gain for the downstream cell segmentation task. The proposed inference method reduces the tiling effects by using 50% fewer computation resources without compromising performance. The proposed random sliding window inference method is a plug-and-play module, which can be generalized and used for other high-resolution WSI image synthesis applications.",
        "authors": "Shunxing Bao, Ho Hin Lee, Qi Yang, Lucas Walker Remedios, Ruining Deng, Can Cui, Leon Yichen Cai, Kaiwen Xu, Xin Yu, Sophie Chiron, Yike Li, Nathan Heath Patterson, Yaohong Wang, Jia Li, Qi Liu, Ken S. Lau, Joseph T. Roland, Lori A. Coburn, Keith T. Wilson, Bennett A. Landman, Yuankai Huo",
        "award": "",
        "id": "146",
        "melba": "False",
        "or_id": "uZl3yA2dBt",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T24",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis",
        "yt_full": ""
    },
    "P147": {
        "abstract": "Deep cascaded architectures for magnetic resonance imaging (MRI) acceleration have shown remarkable success in providing high-quality reconstruction. However, as the number of cascades increases, the improvements in reconstruction tend to become marginal, indicating possible excess model capacity. Knowledge distillation (KD) is an emerging technique to compress these models, in which a trained deep `teacher' network is used to distill knowledge to a smaller `student' network, such that the student  learns to mimic the behavior of the teacher. Most KD methods focus on effectively training the student with a pre-trained teacher that is unaware of the student model. We propose SFT-KD-Recon, a student-friendly teacher training approach along with the student as a prior step to KD to make the teacher aware of the student's structure and capacity and enable aligning the teacher's representations with the student.  In SFT, the teacher is jointly trained with the unfolded branch configurations of the student blocks using three loss terms - teacher-reconstruction loss, student-reconstruction loss, and teacher-student imitation loss, followed by KD of the student. We perform extensive experiments for MRI acceleration in 4x and 5x under-sampling, on the brain and cardiac datasets on five KD methods using the proposed approach as a prior step. We consider the DC-CNN architecture and setup teacher as D5C5 (141765 parameters), and student as D3C5 (49285 parameters) denoting 2.87:1 compression.  Results show that (i) our approach consistently improves the KD methods with improved reconstruction performance and image quality, and (ii) the student distilled using our approach is competitive with the teacher, with the performance gap reduced from 0.53 dB to 0.03 dB.",
        "authors": "NagaGayathri Matcha, Sriprabha Ramanarayanan, Mohammad Al Fahim, Rahul G S, Keerthi Ram, Mohanasankar Sivaprakasam",
        "award": "",
        "id": "147",
        "melba": "False",
        "or_id": "j6GZ2IEia_E",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W18",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image Reconstruction",
        "yt_full": ""
    },
    "P153": {
        "abstract": "Federated learning (FL) can collaboratively train deep learning models using isolated patient data owned by different hospitals for various clinical applications, including medical image segmentation. However, a major problem of FL is its performance degradation when dealing with data that are not independently and identically distributed (non-iid), which is often the case in medical images. In this paper, we first conduct a theoretical analysis on the FL algorithm to reveal the problem of model aggregation during training on non-iid data. With the insights gained through the analysis, we propose a simple yet effective method, federated cross learning (FedCross), to tackle this challenging problem. Unlike the conventional FL methods that combine multiple individually trained local models on a server node, our FedCross sequentially trains the global model across different clients in a round-robin manner, and thus the entire training procedure does not involve any model aggregation steps. To further improve its performance to be comparable with the centralized learning method, we combine the FedCross with an ensemble learning mechanism to compose a federated cross ensemble learning (FedCrossEns) method. Finally, we conduct extensive experiments using a set of public datasets. The experimental results show that the proposed FedCross training strategy outperforms the mainstream FL methods on non-iid data. In addition to improving the segmentation performance, our FedCrossEns can further provide a quantitative estimation of the model uncertainty, demonstrating the effectiveness and clinical significance of our designs. Source code is publicly available at https://github.com/DIAL-RPI/FedCross.",
        "authors": "Xuanang Xu, Hannah H. Deng, Tianyi Chen, Tianshu Kuang, Joshua C. Barber, Daeseung Kim, Jaime Gateno, James J. Xia, Pingkun Yan",
        "award": "",
        "id": "153",
        "melba": "False",
        "or_id": "DrZbwobH_zo",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M27",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Federated Cross Learning for Medical Image Segmentation",
        "yt_full": ""
    },
    "P161": {
        "abstract": "Although deep learning (DL) models have shown great success in many medical image analysis tasks, deployment of the resulting models into  real clinical contexts requires: (1) that they exhibit robustness and fairness across different sub-populations, and (2) that the confidence in DL model predictions be accurately expressed in the form of uncertainties. Unfortunately, recent studies have indeed shown significant biases in DL models across demographic subgroups (e.g., race, sex, age) in the context of medical image analysis, indicating a lack of fairness in the models. Although several methods have been proposed in the ML literature to mitigate a lack of fairness in DL models, they focus entirely on the absolute performance between groups without considering their effect on uncertainty estimation. In this work, we present the first exploration of the effect of popular fairness models on overcoming biases across subgroups in medical image analysis in terms of bottom-line performance, and their effects on uncertainty quantification. We perform extensive experiments on three different clinically relevant tasks: (i) skin lesion classification, (ii) brain tumour segmentation, and (iii) Alzheimer's disease clinical score regression. Our results indicate that popular ML methods, such as data-balancing and distributionally robust optimization, succeed in mitigating fairness issues in terms of the model performances for some of the tasks. However, this can come at the cost of poor uncertainty estimates associated with the model predictions. This tradeoff must be mitigated if fairness models are to be adopted in medical image analysis. ",
        "authors": "Raghav Mehta, Changjian Shui, Tal Arbel",
        "award": "",
        "id": "161",
        "melba": "False",
        "or_id": "Pd4_U5ZzEY",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T25",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis",
        "yt_full": ""
    },
    "P162": {
        "abstract": "The automation of chest X-ray reporting has garnered significant interest due to the time-consuming nature of the task. However, the clinical accuracy of free-text reports has proven challenging to quantify using natural language processing metrics, given the complexity of medical information, the variety of writing styles, and the potential for typos and inconsistencies. Structured reporting and standardized reports, on the other hand, can provide consistency and formalize the evaluation of clinical correctness. However, high-quality annotations for structured reporting are scarce. Therefore, we propose a method to predict clinical findings defined by sentences in structured reporting templates, which can be used to fill such templates. The approach involves training a contrastive language-image model using chest X-rays and related free-text radiological reports, then creating textual prompts for each structured finding and optimizing a classifier to predict clinical findings in the medical image. Results show that even with limited image-level annotations for training, the method can accomplish the structured reporting tasks of severity assessment of cardiomegaly and localizing pathologies in chest X-rays.",
        "authors": "Matthias Keicher, Kamilia Zaripova, Tobias Czempiel, Kristina Mach, Ashkan Khakzar, Nassir Navab",
        "award": "",
        "id": "162",
        "melba": "False",
        "or_id": "wiN5LQThnIV",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W19",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays",
        "yt_full": ""
    },
    "P164": {
        "abstract": "Image-to-image translation (I2I) methods allow the generation of artificial images that share the content of the original image but have a different style. With the advances in Generative Adversarial Networks (GANs)-based methods, I2I methods enabled the generation of artificial images that are indistinguishable from natural images. Recently, I2I methods were also employed in histopathology for generating artificial images of in silico stained tissues from a different type of staining. We refer to this process as stain transfer. The number of I2I variants is constantly increasing, which makes a well justified choice of the most suitable I2I methods for stain transfer challenging. In our work, we compare twelve stain transfer approaches, three of which are based on traditional and nine on GAN-based image processing methods. The analysis relies on complementary quantitative measures for the quality of image translation, the assessment of the suitability for deep learning-based tissue grading, and the visual evaluation by pathologists. Our study highlights the strengths and weaknesses of the stain transfer approaches, thereby allowing a rational choice of the underlying I2I algorithms. Code, data, and trained models for stain transfer between H&E and Masson's Trichrome staining will be made available online.",
        "authors": "Igor Zingman, Sergio Frayle, Ivan Tankoyeu, Sergey Sukhanov, Fabian Heinemann",
        "award": "",
        "id": "164",
        "melba": "False",
        "or_id": "leVAXRDthXI",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M28",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "A comparative evaluation of image-to-image translation methods for stain transfer in histopathology",
        "yt_full": ""
    },
    "P165": {
        "abstract": "Transformers have shown great success in medical image segmentation. However, transformers may exhibit a limited generalization ability due to the underlying single-scale self-attention (SA) mechanism. In this paper, we address this issue by introducing a Multi-scale hiERarchical vIsion Transformer (MERIT) backbone network, which improves the generalizability of the model by computing SA at multiple scales. We also incorporate an attention-based decoder, namely Cascaded Attention Decoding (CASCADE), for further refinement of multi-stage features generated by MERIT. Finally, we introduce an effective multi-stage feature mixing loss aggregation (MUTATION) method for better model training via implicit ensembling. Our experiments on two widely used medical image segmentation benchmarks (i.e., Synapse Multi-organ, ACDC) demonstrate the superior performance of MERIT over state-of-the-art methods. Our MERIT architecture and MUTATION loss aggregation can be used with downstream medical image and semantic segmentation tasks.",
        "authors": "Md Mostafijur Rahman, Radu Marculescu",
        "award": "",
        "id": "165",
        "melba": "False",
        "or_id": "u0MHV19E2n",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T26",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "/virtual/poster/P165.pdf",
        "title": "Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation",
        "yt_full": "https://youtu.be/DYwsK2lmhm4"
    },
    "P166": {
        "abstract": "Low dose computer tomography (CT) acquisition using reduced radiation or sparse angle measurements is recommended to decrease the harmful effects of X-ray radiation. Recent works successfully apply deep networks to the problem of low dose CT recovery on benchmark datasets. However, their robustness needs a thorough evaluation before use in clinical settings. In this work, we evaluate the robustness of different deep learning  approaches and classical methods for CT recovery.We show that deep networks, including model based networks encouraging data consistency are more susceptible to untargeted attacks. Surprisingly, we observe that data consistency is not heavily affected  even for these poor quality reconstructions, motivating the need for better regularization for the networks. We demonstrate the feasibility of  universal attacks and study attack transferability across different methods.  We analyze robustness to attacks causing localized changes in clinically relevant regions. Both classical approaches and deep networks are affected by such attacks leading to change in  visual appearance of localized lesions, for extremely small perturbations. As the resulting reconstructions have high data consistency with original measurements, these localized attacks can be used to explore the solution space of CT recovery problem. ",
        "authors": "Kanchana Vaishnavi Gandikota, Paramanand Chandramouli, Hannah Dr\u00f6ge, Michael Moeller",
        "award": "",
        "id": "166",
        "melba": "False",
        "or_id": "L-N1uAxfQk1",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P166.pdf",
        "title": "Evaluating Adversarial Robustness of Low dose CT Recovery",
        "yt_full": "https://youtu.be/2kI-rHBoaIg"
    },
    "P178": {
        "abstract": "Many applications in machine vision and medical imaging require the capture of images from a scene with very low radiance, which may result in very noisy images and videos. An important example of such an application is the imaging of fluorescently-labeled tissue in fluorescence-guided surgery. Medical imaging systems, especially when intended to be used in surgery, are designed to operate in well-lit environments and use optical filters, time division, or other strategies that allow the simultaneous capture of low radiance fluorescence video and a well-lit visible light video of the scene. This work demonstrates video denoising can be dramatically improved by utilizing deep learning together with motion and textural cues from the noise-free video.",
        "authors": "Trevor Seets, Wei Lin, Yizhou Lu, Christie Lin, Adam Uselmann, Andreas Velten",
        "award": "",
        "id": "178",
        "melba": "False",
        "or_id": "TcUtCXRcK8",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M29",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "OFDVDnet: A Sensor Fusion Approach for Video Denoising in Fluorescence-Guided Surgery",
        "yt_full": ""
    },
    "P179": {
        "abstract": "Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations. In this paper, we propose a novel Inherent Consistent Learning (ICL) method, aims to learn robust semantic category representations through the semantic consistency guidance of labeled and unlabeled data to help segmentation. In practice, we introduce two external modules, namely Supervised Semantic Proxy Adaptor (SSPA) and Unsupervised Semantic Consistent Learner (USCL) that is based on the attention mechanism to align the semantic category representations of labeled and unlabeled data, as well as update the global semantic representations over the entire training set. The proposed ICL is a plug-and-play scheme for various network architectures, and the two modules are not involved in the testing stage. Experimental results on three public benchmarks show that the proposed method can outperform the state-of-the-art, especially when the number of annotated data is extremely limited. Code is available at: https://github.com/zhuye98/ICL.git",
        "authors": "Ye Zhu, Jie Yang, Siqi Liu, Ruimao Zhang",
        "award": "",
        "id": "179",
        "melba": "False",
        "or_id": "diXhe9kUnQ",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "",
        "title": "Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation",
        "yt_full": ""
    },
    "P181": {
        "abstract": "Integrating multi-modal data to promote medical image analysis has recently gained great attention. This paper presents a novel scheme to learn the mutual benefits of different modalities to achieve better segmentation results for unpaired multi-modal medical images. Our approach tackles two critical issues of this task from a practical perspective: (1) how to effectively learn the semantic consistencies of various modalities (e.g., CT and MRI), and (2) how to leverage the above consistencies to regularize the network learning while preserving its simplicity. To address (1), we leverage a carefully designed External Attention Module (EAM) to align semantic class representations and their correlations of different modalities. To solve (2), the proposed EAM is designed as an external plug-and-play one, which can be discarded once the model is optimized. We have demonstrated the effectiveness of the proposed method on two medical image segmentation scenarios: (1) cardiac structure segmentation, and (2) abdominal multi-organ segmentation. Extensive results show that the proposed method outperforms its counterparts by a wide margin.",
        "authors": "Jie Yang, Ye Zhu, Chaoqun Wang, Zhen Li, Ruimao Zhang",
        "award": "",
        "id": "181",
        "melba": "False",
        "or_id": "e9qGhrfP1v",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T27",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Toward Unpaired Multi-modal Medical Image Segmentation via Learning Structured Semantic Consistency",
        "yt_full": ""
    },
    "P185": {
        "abstract": "Diffusion Probabilistic Model (DPM) has recently become one of the hottest topics in computer vision. Its image generation applications, such as Imagen, Latent Diffusion Models, and Stable Diffusion, have demonstrated impressive generation capabilities, which have sparked extensive discussions in the community. Furthermore, many recent studies have found DPM to be useful in a variety of other vision tasks, including image deblurring, super-resolution, and anomaly detection. Inspired by the success of DPM, we propose MedSegDiff, the first DPM-based model for general medical image segmentation tasks. To enhance the step-wise regional attention in DPM for medical image segmentation, we propose Dynamic Conditional Encoding, which establishes state-adaptive conditions for each sampling step. Additionally, we propose the Feature Frequency Parser (FF-Parser) to eliminate the negative effect of high-frequency noise components in this process. We verify the effectiveness of MedSegDiff on three medical segmentation tasks with different image modalities, including optic cup segmentation over fundus images, brain tumor segmentation over MRI images, and thyroid nodule segmentation over ultrasound images. Our experimental results show that MedSegDiff outperforms state-of-the-art (SOTA) methods by a considerable performance gap, demonstrating the generalization and effectiveness of the proposed model.",
        "authors": "Junde Wu, RAO FU, Huihui Fang, Yu Zhang, Yehui Yang, Haoyi Xiong, Huiying Liu, Yanwu Xu",
        "award": "",
        "id": "185",
        "melba": "False",
        "or_id": "Jdw-cm2jG9",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W57",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model",
        "yt_full": ""
    },
    "P187": {
        "abstract": "The growing number of subtypes and treatment options for oropharyngeal squamous cell carcinoma (OPSCC), a common type of head and neck cancer (HNC), highlights the need for personalized therapies. Prognostic outcome prediction models can identify different risk groups for investigation of intensified or de-escalated treatment strategies. Convolution neural networks (CNNs) have been shown to have improved predictive performance compared to traditional clinical and radiomics models by extracting comprehensive and representative features. However, CNNs are limited in their ability to learn global features within an entire volume. In this study, we propose a Transformer-based model for predicting recurrence-free survival (RFS) in OPSCC patients, called TransRP. TransRP consists of a CNN encoder to extract rich PET/CT image features, a Transformer encoder to learn global context features, and a fully connected network to incorporate clinical data for RFS prediction. We investigated three different methods for combining clinical features into TransRP. The experiments were conducted using the public HECKTOR 2022 challenge dataset, which includes pretreatment PET/CT scans, Gross Tumor Volume masks, clinical data, and RFS for OPSCC patients. The dataset was split into a test set (n = 120) and a training set (n = 362) for five-fold cross-validation. The results show that TransRP achieved the highest test concordance index of 0.698 (an improvement > 2%) in RFS prediction compared to several state-of-the-art clinical and CNN-based methods. In addition, we found that incorporating clinical features with image features obtained from the Transformer encoder performed better than using the Transformer encoder to extract features from both clinical and image features. The code for this study is available at (anonymized temporarily for review).",
        "authors": "Baoqiang Ma, Jiapan Guo, Lisanne Van Dijk, P.M.A. van Ooijen, Stefan Both, Nanna Maria Sijtsema",
        "award": "",
        "id": "187",
        "melba": "False",
        "or_id": "eF_6td_piu-",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P187.pdf",
        "title": "TransRP: Transformer-based PET/CT feature extraction incorporating clinical data for recurrence-free survival prediction in oropharyngeal cancer",
        "yt_full": "https://youtu.be/eVOpC1VjZms"
    },
    "P189": {
        "abstract": "Digital pathology based on whole slide images (WSIs) plays a key role in cancer diagnosis and clinical practice. Due to the high resolution of the WSI and unavailability of patch level annotations, WSI classification is usually formulated as a weakly supervised problem, which relies on multiple instance learning (MIL) based on patches of a WSI. In this paper, we aim to learn an optimal patch-level feature space by integrating prototype learning with MIL. To this end, we develop a Trainable Prototype enhanced deep MIL (TPMIL) framework for weakly supervised WSI classification. In contrast to the conventional methods which rely on a certain number of selected patches for feature space refinement, we softly cluster all the instances by allocating them to their corresponding prototypes. Additionally, our method is able to reveal the correlations between different tumor subtypes through distances between corresponding trained prototypes. More importantly, TPMIL also enables to provide a more accurate interpretability based on the distance of the instances from the trained prototypes which serves as an alternative to the conventional attention score-based interpretability. We test our method on two WSI datasets and it achieves a new SOTA.",
        "authors": "Litao Yang, Deval Mehta, Sidong Liu, Dwarikanath Mahapatra, Antonio Di Ieva, Zongyuan Ge",
        "award": "",
        "id": "189",
        "melba": "False",
        "or_id": "P3tSZhxBwJw",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P189.pdf",
        "title": "Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification",
        "yt_full": "https://youtu.be/kXBiZna7TtA"
    },
    "P199": {
        "abstract": "Deep learning models have been effective for various fetal ultrasound segmentation tasks. However, generalization to new unseen data has raised questions about their effectiveness for clinical adoption. Normally, a transition to new unseen data requires time-consuming and costly quality assurance processes to validate the segmentation performance post-transition. Segmentation quality assessment efforts have focused on natural images, where the problem has been typically formulated as a dice score regression task. In this paper, we propose a simplified Fetal Ultrasound Segmentation Quality Assessment (FUSQA) model to tackle the segmentation performance deterioration challenge. We formulate the segmentation quality assessment process as an automated classification task to distinguish between good and poor quality segmentation masks for more accurate gestational age estimation. We validate the performance of our proposed approach on two datasets we collect from two hospitals using different ultrasound machines. We compare different architectures, with our best-performing architecture achieving over 90% classification accuracy on distinguishing between good and poor quality segmentation masks from an unseen dataset. Additionally, there was only a 1.45-days difference between the gestational age reported by doctors and estimated based on CRL measurements using well-segmented masks. On the other hand, this difference increased and reached up to 7.73 days when we calculated CRL from the poorly segmented masks. As a result, AI-based approaches can potentially aid fetal ultrasound segmentation quality assessment and might detect poor segmentation in real-time screening in the future.",
        "authors": "Sevim Cengiz, Ibrahim Almakky, Mohammad Yaqub",
        "award": "",
        "id": "199",
        "melba": "False",
        "or_id": "Umyz5JHIXpD",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P199.pdf",
        "title": "FUSQA: Fetal Ultrasound Segmentation Quality Assessment",
        "yt_full": "https://youtu.be/aTh7U5oZn2I"
    },
    "P200": {
        "abstract": "Recently, deep learning has produced encouraging results for kidney stone classification using endoscope images. However, the shortage of annotated training data poses a severe problem in improving the performance and generalization ability of the trained model. It is thus crucial to fully exploit the limited data at hand. In this paper, we propose SegPrompt to alleviate the data shortage problems by exploiting segmentation maps from two aspects. First, SegPrompt integrates segmentation maps to facilitate classification training so that the classification model is aware of the regions of interest. The proposed method allows the image and segmentation tokens to interact with each other to fully utilize the segmentation map information. Second, we use the segmentation maps as prompts to tune the pretrained deep model, resulting in much fewer trainable parameters than vanilla finetuning. We perform extensive experiments on the collected kidney stone dataset. The results show that SegPrompt can achieve an advantageous balance between the model fitting ability and the generalization ability, eventually leading to an effective model with limited training data.",
        "authors": "Wei Zhu, Runtao Zhou, Yuan Yao, Timothy Douglas Campbell, Rajat Kumar Jain, Jiebo Luo",
        "award": "",
        "id": "200",
        "melba": "False",
        "or_id": "QXjGotk45lb",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P200.pdf",
        "title": "SegPrompt: Using Segmentation Map as a Better Prompt to Finetune Deep Models for Kidney Stone Classification",
        "yt_full": "https://youtu.be/h7haeTLoBOg"
    },
    "P201": {
        "abstract": "Currently, most state-of-the-art pipelines for 3D micro-connectomic reconstruction deal with neuron over-segmentation, agglomeration and subcellular compartment (nuclei, mitochondria, synapses, etc.) detection separately. Inspired by the proofreading consensus of experts, we established a paradigm to acquire priori knowledge of cellular characteristics and ultrastructures, as well as determine the connectivity of neural circuits simultaneously. Following this novel paradigm, we were keen to bring the Intra- and Inter-Cellular Awareness back when Tracking and Segmenting neurons in connectomics. Our proposed method (II-CATS) utilizes few-shot learning techniques to encode the internal neurite representation and its learnable components, which could significantly impact neuron tracings. We further go beyond the original expected run length (ERL) metric by focusing on biological constraints (bERL) or spanning from the nucleus to spines (nERL). With the evaluation of these metrics, we perform typical experiments on multiple electron microscopy datasets on diverse animals and scales. In particular, our proposed method is naturally suitable for tracking neurons that have been identified by staining.",
        "authors": "Hao Zhai, Jing Liu, Bei Hong, Jiazheng Liu, Qiwei Xie, Hua Han",
        "award": "",
        "id": "201",
        "melba": "False",
        "or_id": "3_qtVh7gTyy",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "",
        "title": "Intra- and Inter-Cellular Awareness for 3D Neuron Tracking and Segmentation in Large-Scale Connectomics",
        "yt_full": ""
    },
    "P204": {
        "abstract": "Fast spin-echo (FSE) pulse sequences for Magnetic Resonance Imaging (MRI) offer important imaging contrast in clinically feasible scan times. T2-shuffling is widely used to resolve temporal signal dynamics in FSE acquisitions by exploiting temporal correlations via linear latent space and a predefined regularizer. However, predefined regularizers fail to exploit the incoherence especially for 2D acquisitions.Recent self-supervised learning methods achieve high-fidelity reconstructions by learning a regularizer from undersampled data without a standard supervised training data set. In this work, we propose a novel approach that utilizes a self supervised learning framework to learn a regularizer constrained on a linear latent space which improves time-resolved FSE images reconstruction quality. Additionally, in regimes without groundtruth sensitivity maps, we propose joint estimation of coil-sensitivity maps using an iterative reconstruction technique. Our technique functions is in a zero-shot fashion, as it only utilizes data from a single scan of highly undersampled time series images. We perform experiments on simulated and retrospective in-vivo data to evaluate the performance of the proposed zero-shot learning method for temporal FSE reconstruction. The results demonstrate the success of our proposed method where NMSE and SSIM are significantly increased and the artifacts are reduced.",
        "authors": "Molin Zhang, Junshen Xu, Yamin Arefeen, Elfar Adalsteinsson",
        "award": "",
        "id": "204",
        "melba": "False",
        "or_id": "gaMVPWvzF1d",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W21",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent Space",
        "yt_full": ""
    },
    "P212": {
        "abstract": "Magnetic Resonance Imaging (MRI) is considered the gold standard of medical imaging because of the excellent soft-tissue contrast exhibited in the images reconstructed by the MR pipeline, which enables the human radiologist to discern many pathologies easily. More recently, Deep Learning (DL) models have also achieved state-of-the-art performance in diagnosing multiple diseases using these reconstructed images as input. However, the image reconstruction process within the MR pipeline, which requires the use of complex hardware and adjustment of a large number of scanner parameters, is highly susceptible to the noise of various forms resulting in arbitrary artifacts within the images. Furthermore, the noise distribution is not stationary and varies within a machine, across machines, and patients, leading to varying artifacts within the images. Unfortunately, DL models are quite sensitive to these varying artifacts as it leads to changes in the input data distribution between the training and testing phases. The lack of robustness of these models against varying artifacts impedes their use in medical applications where safety is critical. In this work, we focus on improving the generalization performance of these models in the presence of multiple varying artifacts that manifest due to the complexity of the MR data acquisition. In our experiments, we observe that Batch Normalization (BN), a widely used technique during the training of DL models for medical image analysis, is a significant cause of performance degradation in these changing environments. As a solution, we propose to use other normalization techniques, such as Group Normalization (GN) and Layer Normalization (LN), to inject robustness into model performance against varying image artifacts. Through a systematic set of experiments, we show that GN and LN provide better accuracy for various MR artifacts and distribution shifts.",
        "authors": "Divyam Madaan, Daniel Sodickson, Kyunghyun Cho, Sumit Chopra",
        "award": "",
        "id": "212",
        "melba": "False",
        "or_id": "iA0XwM0IU08",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M30",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis",
        "yt_full": ""
    },
    "P213": {
        "abstract": "Selective experience replay is a popular strategy for integrating lifelong learning with deep reinforcement learning. Selective experience replay aims to recount selected experiences from previous tasks to avoid catastrophic forgetting. Furthermore, selective experience replay based techniques are model agnostic and allow experiences to be shared across different models. However, storing experiences from all previous tasks make lifelong learning using selective experience replay computationally very expensive and impractical as the number of tasks increase. To that end, we propose a reward distribution-preserving coreset compression technique for compressing experience replay buffers stored for selective experience replay.   We evaluated the coreset lifelong deep reinforcement learning technique on the brain tumor segmentation (BRATS) dataset for the task of ventricle localization and on the whole-body MRI for localization of left knee cap, left kidney, right trochanter, left lung, and spleen. The coreset lifelong learning models trained on a sequence of 10 different brain MR imaging environments demonstrated excellent performance localizing the ventricle with a mean pixel error distance of 12.93, 13.46, 17.75, and 18.55 for the compression ratios of 10x, 20x, 30x, and 40x, respectively. In comparison, the conventional lifelong learning model localized the ventricle with a mean pixel distance of 10.87. Similarly, the coreset lifelong learning models trained on whole-body MRI demonstrated no significant difference (p=0.28) between the 10x compressed coreset lifelong learning models and conventional lifelong learning models for all the landmarks. The mean pixel distance for the 10x compressed models across all the landmarks was 25.30, compared to 19.24 for the conventional lifelong learning models. Our results demonstrate that the potential of the coreset-based ERB compression method for compressing experiences without a significant drop in performance.",
        "authors": "Guangyao Zheng, Samson Zhou, Vladimir Braverman, Michael A. Jacobs, Vishwa Sanjay Parekh",
        "award": "",
        "id": "213",
        "melba": "False",
        "or_id": "pP3p0rmeyRb",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T28",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging",
        "yt_full": ""
    },
    "P215": {
        "abstract": "Histopathology is critical for the diagnosis of many diseases, including cancer. These protocols typically require pathologists to manually evaluate slides under a microscope, which is time-consuming and subjective, leading to interest in machine learning to automate analysis. However, computational techniques are limited by batch effects, where technical factors like differences in preparation protocol or scanners can alter the appearance of slides, causing models trained on one institution or patient to fail when generalizing to others. Here, we propose a domain adaptation method that improves the generalization of histopathological models to data from unseen institutions, without the need for labels or retraining in these new settings. Our approach introduces an optimal transport (OT) loss, that extends adversarial methods that penalize models if images from different institutions can be distinguished in their representation space. Unlike previous methods, which operate on single samples, our loss accounts for distributional differences between batches of images. We show that on the Camelyon17 dataset, while both methods can adapt to global differences in color distribution, only our OT loss can reliably classify a cancer phenotype unseen during training. Together, our results suggest that OT improves generalization on rare but critical phenotypes that may only make up a small fraction of the total tiles and variation in a slide.  ",
        "authors": "Kianoush Falahkheirkhah, Alex Xijie Lu, David Alvarez-Melis, Grace Huynh",
        "award": "",
        "id": "215",
        "melba": "False",
        "or_id": "nmZRTaZZv5Z",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P215.pdf",
        "title": "Domain adaptation using optimal transport for invariant learning using histopathology datasets",
        "yt_full": "https://youtu.be/GMYL3fUc2_o"
    },
    "P217": {
        "abstract": "Anomaly detection remains a challenging task in neuroimaging when little to no supervision is available and when lesions can be very small or with subtle contrast. Patch-based representation learning has shown powerful representation capacities when applied to industrial or medical imaging and outlier detection methods have been applied successfully to these images. In this work, we propose an unsupervised anomaly detection (UAD) method based on a latent space constructed by a siamese patch-based auto-encoder and perform the outlier detection with a One-Class SVM training paradigm tailored to the lesion detection task in multi-modality neuroimaging. We evaluate performances of this model on a public database, the White Matter Hyperintensities (WMH) challenge and show in par performance with the two best performing state-of-the-art methods reported so far. ",
        "authors": "Nicolas Pinon, Robin Trombetta, Carole Lartizien",
        "award": "",
        "id": "217",
        "melba": "False",
        "or_id": "_c9r6-HCEaN",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P217.pdf",
        "title": "One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities",
        "yt_full": "https://youtu.be/koaJ808Gtkw"
    },
    "P218": {
        "abstract": "Recent applications of deep convolutional neural networks in medical imaging raise concerns about their interpretability. While most explainable deep learning applications use post hoc methods (such as GradCAM) to generate feature attribution maps, there is a new type of case-based reasoning models, namely ProtoPNet and its variants, which identify prototypes during training and compare input image patches with those prototypes. We propose the first medical prototype network (MProtoNet) to extend ProtoPNet to brain tumor classification with 3D multi-parametric magnetic resonance imaging (mpMRI) data. To address different requirements between 2D natural images and 3D mpMRIs especially in terms of localizing attention regions, a new attention module with soft masking and online-CAM loss is introduced. Soft masking helps sharpen attention maps, while online-CAM loss directly utilizes image-level labels when training the attention module. MProtoNet achieves statistically significant improvements in interpretability metrics of both correctness and localization coherence (with a best activation precision of $0.713\\pm0.058$) without human-annotated labels during training, when compared with GradCAM and several ProtoPNet variants. The source code is available at https://github.com/aywi/mprotonet.",
        "authors": "Yuanyuan Wei, Roger Tam, Xiaoying Tang",
        "award": "",
        "id": "218",
        "melba": "False",
        "or_id": "6Wbj3QCo4U4",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "False",
        "slides": "/virtual/poster/P218.pdf",
        "title": "MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging",
        "yt_full": "https://youtu.be/DtfXwrliVQg"
    },
    "P219": {
        "abstract": "Digital Breast Tomosynthesis (DBT) is an X-ray imaging modality enabling the reconstruction of 3D volumes of breasts. DBT is mainly used for cancer screening, and is intended to replace conventional mammography in the coming years. However, DBT reconstructions are impeded by several types of artefacts induced by the geometry of the device itself, degrading the image quality and limiting its resolution along the thickness of the compressed breast. In this study, we propose a deep-learning-based pipeline to address the DBT reconstruction problem, focusing on the removal of sparse-view and limited-angle artefacts. Specifically, this procedure is composed of two steps: a classic reconstruction algorithm is first applied on normalised projections, then a deep neural network is tasked with erasing the artefacts present in the obtained volumes. A major difficulty to solve our problem is the lack of real conditions artefact-free data. To overcome this complication, we resort to a new dataset comprised of synthetic breast texture phantoms. We then show that our training method and database strategy are promising to tackle the problem as they improve the informational value of planes orthogonal to the detector, which are not currently used by radiologists due to their poor quality. Eventually, we assess the impact of removing the bias components from the network and using stacks of slices as inputs, with regard to the generalisation ability of our approach on both synthetic and clinical data.",
        "authors": "Arnaud Quillent, Vincent Jonas Bismuth, Isabelle Bloch, Christophe Kervazo, Said Ladjal",
        "award": "",
        "id": "219",
        "melba": "False",
        "or_id": "xcMTcyk2v69",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W16",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "A deep learning method trained on synthetic data for digital breast tomosynthesis reconstruction",
        "yt_full": ""
    },
    "P222": {
        "abstract": "Deep learning is the state-of-the-art for medical imaging tasks, but requires large, labeled datasets. For risk prediction, large datasets are rare since they require both imaging and follow-up (e.g., diagnosis codes). However, the release of publicly available imaging data with diagnostic labels presents an opportunity for self and semi-supervised approaches to improve label efficiency for risk prediction. Though several studies have compared self-supervised approaches in natural image classification, object detection, and medical image interpretation, there is limited data on which approaches learn robust representations for risk prediction. We present a comparison of semi- and self-supervised learning to predict mortality risk using chest x-ray images. We find that a semi-supervised autoencoder outperforms contrastive and transfer learning in internal and external validation. ",
        "authors": "Yanru Chen, Michael T Lu, Vineet K Raghu",
        "award": "",
        "id": "222",
        "melba": "False",
        "or_id": "HzpdwXFc_Q",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W55",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "A comparison of self-supervised pretraining approaches for predicting disease risk from chest radiograph images",
        "yt_full": ""
    },
    "P229": {
        "abstract": "Image segmentation is at the core of many tasks in medical imaging, including the engineering of computational biomarkers. While the self-configuring nnUNet framework for image segmentation tasks completely shifted the state-of-the-art in the radiology field, it has never been adapted to overcome its limitations for application on the pathology domain. Our study showcases the potential of nnUNet in computational pathology and bridges the gap that currently exists in utilizing nnUNet for pathology applications. Our proposed nnUNet for pathology framework has demonstrated its significance and potential to shift the state-of-the-art in the computational pathology field, as seen from the exceptional first-place segmentation ranking on the TIGER challenge's experimental leaderboard 1. Our framework includes critical hyperparameter adjustments and pathology-specific color augmentations, as well as an essential WSI inference pipeline and a novel inference uncertainty approach that proves helpful for biomarker development. We release the code of our accurate and workflow-friendly segmentation tool to promote and foster growth within the computational pathology community.",
        "authors": "Joey Spronck, Thijs Gelton, Leander van Eekelen, Joep Bogaerts, Leslie Tessier, Mart van Rijthoven, Lieke van der Woude, Michel van den Heuvel, Willemijn Theelen, Jeroen van der Laak, Francesco Ciompi",
        "award": "",
        "id": "229",
        "melba": "False",
        "or_id": "aHuwlUu_QR",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M31",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "nnUNet meets pathology: Bridging the gap for application to whole slide images and computational biomarkers",
        "yt_full": ""
    },
    "P235": {
        "abstract": "Positron emission tomography (PET) is an important functional medical imaging technique often used in the evaluation of certain brain disorders, whose reconstruction problem is ill-posed. The vast majority of reconstruction methods in PET imaging, both iterative and deep learning, return a single estimate without quantifying the associated uncertainty. Due to ill-posedness and noise, a single solution can be misleading or inaccurate. Thus, providing a measure of uncertainty in PET image reconstruction can help medical practitioners in making critical decisions. This paper proposes a deep learning-based method for uncertainty quantification in PET image reconstruction via posterior sampling. The method is based on training a conditional generative adversarial network whose generator approximates sampling from the posterior in Bayesian inversion. The generator is conditioned on reconstruction from a low-dose PET scan obtained by a conventional reconstruction method and a high-quality magnetic resonance image and learned to estimate a corresponding standard-dose PET scan reconstruction. We show that the proposed model generates high-quality posterior samples and yields physically-meaningful uncertainty estimates.",
        "authors": "Tin Vla\u0161i\u0107, Tomislav Matuli\u0107, Damir Ser\u0161i\u0107",
        "award": "",
        "id": "235",
        "melba": "False",
        "or_id": "oqPQke7xz2",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T30",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Estimating Uncertainty in PET Image Reconstruction via Deep Posterior Sampling",
        "yt_full": ""
    },
    "P237": {
        "abstract": "Lateral ventricular volume (LVV) is an important biomarker for clinical investigation. We present the first transfer learning-based LVV segmentation method for fluid-attenuated inversion recovery (FLAIR) MRI. To mitigate covariate shifts between source and target domains, this work proposes an domain adaptation method that optimizes performance on three target datasets. Silver standard (SS) masks were generated from the target domain using a novel conventional image processing ventricular segmentation algorithm and used to supplement the gold standard (GS) data from the source domain, Canadian Atherosclerosis Imaging Network (CAIN). Four models were tested on held-out test sets from four datasets: 1) SS+GS: trained on target SS masks and fine-tuned on source GS masks, 2) GS+SS: trained on source GS masks and fine-tuned on target SS masks, 3) trained on source GS (GS CAIN Only) and 4) trained on target SS masks (SS Only). The SS+GS model had the best and most consistent performance (mean DSC = 0.89, CoV = 0.05) and showed significantly p < 0.05) higher DSC compared to the GS-only model on three target domains. Results suggest pre-training with noisy labels from the target domain allows the model to adapt to the dataset-specific characteristics and provides robust parameter initialization while fine-tuning with GS masks allows the model to learn detailed features. This method has wide application to other medical imaging problems where labeled data is scarce, and can be used as a per-dataset calibration method to accelerate wide-scale adoption. ",
        "authors": "Owen Crystal, April Khademi, Alan R Moody, Pejman J Maralani, Sandra E Black",
        "award": "",
        "id": "237",
        "melba": "False",
        "or_id": "Ed-9HCu7l2r",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W23",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Domain Adaptation using Silver Standard Masks for Lateral Ventricle Segmentation in FLAIR MRI",
        "yt_full": ""
    },
    "P238": {
        "abstract": "Statistical Shape Modelling (SSM) is an effective tool for quantitatively analyzing anatom- ical populations. SSM has benefitted largely from advances in deep learning where statis- tical representations of anatomies (e.g., point distribution models or PDMs) are inferred directly from images, alleviating the need for a time-consuming and expensive workflow of anatomy segmentation, shape registration, and model optimization. Nonetheless, to date, existing deep learning methods do not consider the rigid pose transformation of shapes or anatomy of interest. They also require a tight bounding box to be defined over the image of anatomy-of-interest before feeding the image to the deep network for network training and inference. In this paper, we propose a deep learning framework that simultaneously detects and segments the anatomy of interest, estimate the rigid transformation with respect to the population mean (average) using a spatial transformer, and estimates the corresponding statistical representation of that anatomy, all directly from unsegmented 3D image without the need for any additional supervision. Furthermore, we leverage the segmentation task to provide an attention model for the sub-network that estimates shape representation, giving more accurate shape statistics for shape analysis.",
        "authors": "Janmesh Ukey, Shireen Elhabian",
        "award": "",
        "id": "238",
        "melba": "False",
        "or_id": "oRA8BPiduI",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M32",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Image2SSM: Localization-aware Deep Learning Framework for Statistical Shape Modeling Directly from Images",
        "yt_full": ""
    },
    "P243": {
        "abstract": "Deep learning (DL) methods for white matter lesion (WML) segmentation in MRI suffer a reduction in performance when applied on data from a scanner or centre that is out-of-distribution (OOD) from the training data. This is critical for translation and widescale adoption, since current models cannot be readily applied to data from new institutions.  In this work, we evaluate several intensity standardization methods for MRI as a preprocessing step for WML segmentation in multi-centre Fluid-Attenuated Inversion Recovery (FLAIR) MRI. We evaluate a method specifically developed for FLAIR MRI called IAMLAB along with other popular normalization techniques such as White-strip, Nyul and Z-score. We proposed an Ensemble model that combines predictions from each of these models.  A skip-connection UNet (SC UNet) was trained on the standardized images, as well as the original data and segmentation performance was evaluated over several dimensions. The training (in-distribution) data consists of a single study, of 60 volumes, and the test (OOD) data is 128 unseen volumes from three clinical cohorts. Results show IAMLAB and Ensemble provide higher WML segmentation performance compared to models from original data or other normalization methods. IAMLAB & Ensemble have the highest dice similarity coefficient (DSC) on the in-distribution data (0.78 & 0.80) and on clinical OOD data. DSC was significantly higher for IAMLAB compared to the original data (p<0.05) for all lesion categories (LL>25mL: 0.77 vs. 0.71; 10mL<= LL<25mL: 0.66 vs. 0.61; LL<10mL: 0.53 vs. 0.52).  The IAMLAB and Ensemble normalization methods are mitigating MRI domain shift and are optimal for DL-based WML segmentation in unseen FLAIR data.",
        "authors": "Abdollah Ghazvanchahi, Pejman Jahbedar Maralani, Alan R. Moody, April Khademi",
        "award": "",
        "id": "243",
        "melba": "False",
        "or_id": "sp-X1rXn8_",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T31",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "False",
        "slides": "",
        "title": "Effect of Intensity Standardization on Deep Learning for WML Segmentation in Multi-Centre FLAIR MRI",
        "yt_full": ""
    },
    "S001": {
        "abstract": "One area of medical imaging that has recently experienced innovative deep learning advances is diffusion MRI (dMRI) streamline tractography with recurrent neural networks (RNNs). Unlike traditional imaging studies which utilize voxel-based learning, these studies model dMRI features at points in continuous space off the voxel grid in order to propagate streamlines, or virtual estimates of axons. However, implementing such models is non-trivial, and an open-source implementation is not yet widely available. Here, we describe a series of considerations for implementing tractography with RNNs and demonstrate they allow one to approximate a deterministic streamline propagator with comparable performance to existing algorithms. We release this trained model and the associated implementations leveraging popular deep learning libraries. We hope the availability of these resources will lower the barrier of entry into this field, spurring further innovation.",
        "authors": "Leon Yichen Cai, Ho Hin Lee, Nancy Rose Newlin, Michael Eugene Kim, Daniel Moyer, Francois Rheault, Kurt Gregory Schilling, Bennett A. Landman",
        "award": "",
        "id": "1",
        "melba": "False",
        "or_id": "icYc_uKOI6o",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M33",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Implementation considerations for deep learning with diffusion MRI streamline tractography",
        "yt_full": ""
    },
    "S002": {
        "abstract": "Magnetic resonance imaging (MRI) is widely employed for diagnostic tests in neurology. However, the utility of MRI is largely limited by its long acquisition time. Acquiring fewer k-space data in a sparse manner is a potential solution to reducing the acquisition time, but it can lead to severe aliasing reconstruction artifacts. In this paper, we present a novel Dual-Domain Cross-Iteration Squeeze and Excitation Network (DD-CISENet) for accelerated sparse MRI reconstruction. The information of k-spaces and MRI images can be iteratively fused and maintained using the Cross-Iteration Residual connection (CIR) structures. This study included 720 multi-coil brain MRI cases adopted from the open-source fastMRI Dataset \\cite{zbontar2018fastmri}. Results showed that the average reconstruction error by DD-CISENet was 2.28 \u00b1 0.57%, which outperformed existing deep learning methods including image-domain prediction (6.03 \u00b1 1.31%, p < 0.001), k-space synthesis (6.12 \u00b1 1.66%, p < 0.001), and dual-domain feature fusion approaches (4.05 \u00b1 0.88%, p < 0.001).",
        "authors": "Xiongchao Chen, Zhigang Peng, Gerardo Hermosillo",
        "award": "",
        "id": "2",
        "melba": "False",
        "or_id": "3dRs49a1Xmt",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T32",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "DD-CISENet: Dual-Domain Cross-Iteration Squeeze and Excitation Network for Accelerated MRI Reconstruction",
        "yt_full": ""
    },
    "S003": {
        "abstract": "Steatosis is a common liver disease characterized by the accumulation of lipid droplets in cells. Precise and reliable fat droplet identification is essential for automatic steatosis quantification in histological images. We trained a nnU-Net to automatically segment lipid vacuoles in whole-slide images using semi-automatically generated reference annotations. We evaluated the performance of the trained model on two out-of-distribution datasets. The trained model\u2019s average F1 scores (0.801 and 0.804) suggest a high potential of the nnU-Net framework for the automatic segmentation of lipid vacuoles.",
        "authors": "Daniel Budelmann, Cao Qing, Hendrik Oliver Laue, Mohamed Albadry, Uta Dahmen, Lars Ole Schwen",
        "award": "",
        "id": "3",
        "melba": "False",
        "or_id": "nTnAm_El0RC",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W24",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Segmentation of Lipid Droplets in Histological Images",
        "yt_full": ""
    },
    "S004": {
        "abstract": "Cardiac MR segmentation using deep learning has advanced significantly. However, inaccurate segmentation results can lead to flawed clinical decisions in downstream tasks. Hence, it is essential to identify failed segmentations through quality control (QC) methods before proceeding with further analysis. This study proposes a fully automatic uncertainty-based QC framework for T1 mapping and extracellular volume (ECV) analysis, consisting of three parts. Firstly, Bayesian Swin transformer-based U-Net was employed to segment cardiac structures from a native and post-contrast T1 mapping dataset (n=295). Secondly, our novel uncertainty-based QC, which utilizes image-level uncertainty features, was used to determine the quality of the segmentation outputs. It achieved a mean area under the ROC curve (AUC) of 0.927 on binary classification and a mean absolute error (MAE) of 0.021 on Dice score regression. The proposed QC significantly outperformed other state-of-the-art uncertainty-based QC methods, especially in predicting segmentation quality from poor-performing models, highlighting its robustness in detecting failed segmentations. Finally, T1 mapping and ECV values were automatically computed after the inaccurate segmentation results were rejected by the QC method, characterizing myocardial tissues of healthy and cardiac pathological cases. The myocardial T1 and ECV values computed from automatic and manual segmentations show an excellent agreement yielding Pearson coefficients of 0.990 and 0.975, respectively. The study results indicate that these automatically computed values can accurately characterize myocardial tissues.",
        "authors": "Tewodros Weldebirhan Arega, St\u00e9phanie Bricq, Fran\u00e7ois Legrand, Alexis Jacquier, Alain Lalande, Fabrice MERIAUDEAU",
        "award": "",
        "id": "4",
        "melba": "False",
        "or_id": "_VKOZO2LHF0",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M34",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Uncertainty-based Quality Controlled T1 Mapping and ECV Analysis using Bayesian Vision Transformer",
        "yt_full": ""
    },
    "S005": {
        "abstract": "Deep learning has made great strides in medical imaging, enabled by hardware advances in GPUs. One major constraint for the development of new models has been the saturation of GPU memory resources during training. This is especially true in computational pathology, where images regularly contain more than 1 billion pixels. These pathological images are traditionally divided into small patches to enable deep learning due to hardware limitations. In this work, we explore whether the shared GPU/CPU memory architecture on the M1 Ultra systems-on-a-chip (SoCs) recently released by Apple, Inc. may provide a solution. These affordable systems (less than \\$5000) provide access to 128 GB of unified memory (Mac Studio with M1 Ultra SoC). As a proof of concept for gigapixel deep learning, we identified tissue from background on gigapixel areas from whole slide images (WSIs). The model was a modified U-Net (4492 parameters) leveraging large kernels and high stride. The M1 Ultra SoC was able to train the model directly on gigapixel images (16000$\\times$64000 pixels, 1.024 billion pixels) with a batch size of 1 using over 100 GB of unified memory for the process at an average speed of 1 minute and 21 seconds per batch with Tensorflow 2/Keras. As expected, the model converged with a high Dice score of 0.989 $\\pm$ 0.005. Training up until this point took 111 hours and 24 minutes over 4940 steps. Other high RAM GPUs like the NVIDIA A100 (largest commercially accessible at 80 GB, $\\sim$\\$15000) are not yet widely available (in preview for select regions on Amazon Web Services at \\$40.96/hour as a group of 8). This study is a promising step towards WSI-wise end-to-end deep learning with prevalent network architectures.",
        "authors": "Lucas Walker Remedios, Leon Yichen Cai, Samuel Remedios, Karthik Ramadass, Aravind Krishnan, Ruining Deng, Can Cui, Shunxing Bao, Lori Coburn, Yuankai Huo, Bennett A. Landman",
        "award": "",
        "id": "5",
        "melba": "False",
        "or_id": "OMsh_JAmyfR",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T33",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Exploring shared memory architectures for end-to-end gigapixel deep learning",
        "yt_full": ""
    },
    "S006": {
        "abstract": "Magnetic resonance angiography (MRA) allows for the non-invasive visualization of vascu- lature in the human body and has been widely used in the hospitals to identify aneurysms and the location of a stroke. Generating MRA using the commonly available T1-weighted (T1w) MRI modality would broaden the possibilities for studying vasculature because T1w is commonly acquired in most neuroimaging datasets, while MRA is not. In this work, we propose a method using the statistical generative model called denoising diffusion prob- abilistic model (DDPM) to tackle the MRA synthesis task. Our experiment shows that by diffusing the high frequency signal, which explains the major signal difference between MRA and T1w, DDPM could successfully synthesize MRA with good quality. The pro- posed method also conditioned score-matching estimation with the high frequency signal of the T1w modality, which enables the accurate one-to-one synthesis between MRA and T1w.",
        "authors": "Haoyu Lan, Kirsten M. Lynch, Arthur W Toga, Jeiran Choupan",
        "award": "",
        "id": "6",
        "melba": "False",
        "or_id": "UT7nTUxpaJZ",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W25",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "High Frequency Structural MRI Signal conditioned MRA Synthesis with Denoising Diffusion probabilistic Model",
        "yt_full": ""
    },
    "S008": {
        "abstract": "Pneumothorax (PTX) is an acute thoracic disease that can be diagnosed with chest radiographs. While deep learning (DL) models have proven effective in identifying PTX on radiographs, they have difficulties in gaining the trust of radiologists if the decision-making logic is unclear. Therefore, various methods have been proposed to explain the PTX diagnostic decision made by DL models. However, several studies indicate that the quality of DL model explanation is suboptimal. This paper introduces a human-guided approach to enhance the existing explanation method. Based on the IoU and Dice between the explanation of model-focusing regions and the ground truth lesion areas, we achieved an increase of 60.6% and 56.5% in Saliency Map, 69.0% and 66.7% in Grad-CAM, and 137.5% and 123.9% in Integrated Gradients.",
        "authors": "Han Yuan, Peng-Tao Jiang, Gangming Zhao",
        "award": "",
        "id": "8",
        "melba": "False",
        "or_id": "_kk8KI8MiRE",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M35",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Human-Guided Design to Explain Deep Learning-based Pneumothorax Classifier",
        "yt_full": ""
    },
    "S009": {
        "abstract": "Mammograms are vital for detecting breast cancer, the most common cancer among women in the US. However, low-quality scans and imaging artifacts can compromise their efficacy. We introduce an automated pipeline to filter low-quality mammograms from large datasets. Our initial dataset of 176,492 mammograms contained an estimated 5.5% lower quality scans with issues like metal coil frames, wire clamps, and breast implants. Manually removing these images is tedious and error-prone. Our two-stage process first uses threshold-based 5-bin histogram filtering to eliminate undesirable images, followed by a variational autoencoder to remove remaining low-quality scans. Our method detects such scans with an F1 Score of 0.8862 and preserves 163,568 high-quality mammograms. We provide results and tools publicly available as open-source software.",
        "authors": "Ryan Zurrin, Neha Goyal, Pablo Bendiksen, Muskaan Manocha, Dan Simovici, Nurit Haspel, Marc Pomplun, Daniel Haehn",
        "award": "",
        "id": "9",
        "melba": "False",
        "or_id": "4E93Xdg98u",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T34",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Outlier Detection for Mammograms",
        "yt_full": ""
    },
    "S010": {
        "abstract": "Multispectral optoacoustic tomography requires image feedback in real-time to locate and identify relevant tissue structures during clinical interventions. Backprojection methods are commonly used for optoacoustic image reconstruction in real-time but only afford imprecise images due to oversimplified modelling assumptions. Herein, we present a deep learning framework, termed DeepMB, that infers optoacoustic images with state-of-the-art quality in 31 ms per image.",
        "authors": "Christoph Dehner, Guillaume Zahnd, Vasilis Ntziachristos, Dominik J\u00fcstel",
        "award": "",
        "id": "10",
        "melba": "False",
        "or_id": "iYbJn-wIvf",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W53",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Deep model-based optoacoustic image reconstruction (DeepMB)",
        "yt_full": ""
    },
    "S012": {
        "abstract": "This study aimed to improve the sensitivity and throughput of spermatozoid screening for identifying rape suspects through DNA profiling, based on microscope cytology Whole Slide Imaging (WSI). To this end, we implemented a WSI-based deep-learning algorithm consisting of a detector/classification ensemble, achieving a mean 3-fold cross-validation F1 score of 0.87 [0.87-0.88] on a dataset of 188 retrospective single-center cytology WSI. Applied to slide label-only annotated test set (positive, negative, and doubtful), we show that our ensemble model is capable of screening slide label groups with excellent sensitivity to even find missed spermatozoids in negative-labeled slides. We hope our approach will be of value for routine forensic spermatozoid screening.",
        "authors": "Rutger RH Fick, Melanie Lubrano, Damien Quignon, Didier Empis, Fabrice Kabile, Saima Ben Hadj",
        "award": "",
        "id": "12",
        "melba": "False",
        "or_id": "xBfUkTq17h7",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M36",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "End-to-End Spermatozoid Detection in Cytology WSI for Forensic Pathology Workflow",
        "yt_full": ""
    },
    "S013": {
        "abstract": "Imaging tasks like segmentation and registration are fundamental in a broad range of medical research studies. These tasks are increasingly solved by machine learning based methods. However, given the heterogeneity of medical imaging modalities, many existing methods are not able to generalize well to new modalities or even slight variations of existing modalities, and only perform well on the type of data they were trained on. Most practitioners have limited training data for a given task, limiting their ability to train generalized networks. To enable neural networks trained on one image type or modality to perform well on other  imaging contrasts, we propose $\\texttt{CIFL}$: contrast invariant feature learning. CIFL uses synthesized images of varying contrasts and artifacts, and an unsupervised loss function, to learn rich contrast-invariant image features. The resulting representation can be used as input to downstream tasks like segmentation or registration given some modality available at training, and subsequently enables performing that task on contrasts not available during training. In this paper, we perform experiments that demonstrate generalizability in brain segmentation and registration.",
        "authors": "Yue Zhi Russ Chua, Adrian V Dalca",
        "award": "",
        "id": "13",
        "melba": "False",
        "or_id": "2oCb0q5TA4Y",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T35",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Contrast Invariant Feature Representations for Segmentation and Registration of Medical Images",
        "yt_full": ""
    },
    "S015": {
        "abstract": "The cardiothoracic ratio (CTR) plays an important role in early detection of cardiac enlargement related diseases in chest X-ray (CXR) examinations. Since its measurement would be time-consuming, its evaluation in clinical practice is done by a visual assessment: it is highly subjective and its robustness is undermined by some acquisition issues such as lung clipping or patient orientation variation. No work addressing the problem of clipped lungs in the CTR estimation has been found in the literature.  For these reasons, aiming for a robust method, we firstly proposed a segmentation-based approach for automatic measurement of the CTR (based only on the lung segmentation mask) able to handle clipped anatomy cases. Secondly, the proposed method was validated on a large dataset allowing us to corroborate earlier research results with manual CTR computation in which the mean CTR increases with the age of the patients and there is a noticeable difference between men and women's CTR. Lastly, a new rotational invariant metric was proposed, showing it to be more robust to different patient orientations.",
        "authors": "Matilde Bodritti, Adriyana Danudibroto, Jan Aelterman",
        "award": "",
        "id": "15",
        "melba": "False",
        "or_id": "kNQvCJC0fad",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W27",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Towards Robust Computation of Cardiothoracic Ratio from Chest X-Ray",
        "yt_full": ""
    },
    "S016": {
        "abstract": "Ultrasound and radar signals are useful for medical imaging due to their non-invasive, non-radiative, low-cost, and accessible nature. However, traditional imaging techniques lack resolution, contrast, and physical interpretation. Quantitative medical imaging is necessary for this purpose, as it provides a visual representation of physical characteristics. However, current techniques have drawbacks, including convergence to local minima and delayed results, which can lead to unsatisfactory outcomes. To address these limitations, we propose a neural network that incorporates the symmetries and properties of the received signals to achieve real-time quantitative mappings of physical properties. Our method achieves high accuracy using several numerical metrics for complex shapes with less than 0.15 seconds per test sample, compared to 0.75-2 hours for the competing method. ",
        "authors": "Tom Sharon, Hila Naaman, Yonathan Eder, Yonina C. Eldar",
        "award": "",
        "id": "16",
        "melba": "False",
        "or_id": "B2Cb5y2A6DJ",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S016.pdf",
        "title": "Real-Time Quantitative Ultrasound and Radar Knowledge-Based Medical Imaging",
        "yt_full": "https://youtu.be/IJH6QYnmvU0"
    },
    "S018": {
        "abstract": "In the present work we describe a novel training scheme for automated radiograph annotation, as used in post-surgical assessment of Total Knee Replacement. As we show experimentally, standard off-the-shelf methods fail to provide high accuracy image annotations for Total Knee Replacement annotation. We instead adopt a U-Net based segmentation style annotator, relax the task by dilating annotations into larger label regions, then progressively erode these label regions back to the base task on a schedule based on training epoch. We demonstrate the advantages of this scheme on a dataset of radiographs with gold-standard expert annotations, comparing against four baseline cases.",
        "authors": "Yehyun Suh, Aleksander Mika, J. Ryan Martin, Daniel Moyer",
        "award": "",
        "id": "18",
        "melba": "False",
        "or_id": "bVC9bi_-t7Y",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T36",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Dilation-Erosion Methods for Radiograph Annotation in Total Knee Replacement",
        "yt_full": ""
    },
    "S020": {
        "abstract": "A confluence of neuroscience and clinical studies suggests that disrupted structural connectivity (SC) and functional connectivity (FC) in the brain is an early signs of neurodegenerative diseases. However, current methods lack the neuroscience foundation to understand how these altered coupling mechanisms contribute to cognitive decline. To address this issue, we spotlight a neural oscillation model that characterizes the behavior of neural oscillators coupled via nerve fibers throughout the brain. Tailored a physics-guided graph neural network (GNN), which can predict self-organized functional fluctuations and generate a novel biomarker for early detection of neurodegeneration through altered SC-FC coupling. Our method outperforms conventional coupling methods, providing higher accuracy and revealing the mechanistic role of coupling alterations in disease progression. We evaluate the biomarker using the ADNI dataset for Alzheimer's disease diagnosis.",
        "authors": "Tingting Dan, Guorong Wu",
        "award": "",
        "id": "20",
        "melba": "False",
        "or_id": "CU82z90ppTS",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M55",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Uncovering Structural-Functional Coupling Alterations for Neurodegenerative Diseases",
        "yt_full": ""
    },
    "S021": {
        "abstract": "Fairness and bias are critical considerations for the effective and ethical use of deep learning models for medical image analysis. Despite this, there has been minimal research on how explainable artificial intelligence (XAI) methods can be leveraged to better understand underlying causes of bias in medical image data. To study this, we trained a convolutional neural network on brain magnetic resonance imaging (MRI) data of 4547 adolescents to predict biological sex. Performance disparities between White and Black racial subgroups were analyzed, and average saliency maps were generated for each subgroup based on sex and race. The model showed significantly higher performance in correctly classifying White males compared to Black males, and slightly higher performance for Black females compared to White females. Saliency maps indicated subgroup-specific differences in brain regions associated with pubertal development, an established confounder in this task, which is also associated with race. These findings suggest that models demonstrating performance disparities can also lead to varying XAI outcomes across subgroups, offering insights into potential sources of bias in medical image data.",
        "authors": "Emma Stanley, Matthias Wilms, Pauline Mouches, Nils Forkert",
        "award": "",
        "id": "21",
        "melba": "False",
        "or_id": "O8RJGtdACWs",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M37",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Exploring the Role of Explainability for Uncovering Bias in Deep Learning-based Medical Image Analysis",
        "yt_full": ""
    },
    "S022": {
        "abstract": "Neural operator learning as a means of mapping between complex function spaces has garnered significant attention in the field of computational science and engineering (CS&E). In this paper, we apply Neural operator learning to the time-of-flight ultrasound computed tomography (USCT) problem. We learn the mapping between time-of-flight (TOF) data and the heterogeneous sound speed field using a full-wave solver to generate the training data. This novel application of operator learning circumnavigates the need to solve the computationally intensive iterative inverse problem. The operator learns the non-linear mapping offline and predicts the heterogeneous sound field with a single forward pass through the model. This is the first time operator learning has been used for ultrasound tomography and is the first step in potential real-time predictions of soft tissue distribution for tumor identification in beast imaging.",
        "authors": "Haocheng Dai, Michael Penwarden, Robert Kirby, Sarang Joshi",
        "award": "",
        "id": "22",
        "melba": "False",
        "or_id": "tSokLyjvW5",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T37",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Neural Operator Learning for Ultrasound Tomography Inversion",
        "yt_full": ""
    },
    "S023": {
        "abstract": "Immunohistochemistry based companion diagnosis relies on the examination of single biomarkers for patient stratification. However, recent years have seen an increasing need to characterize the interactions among biomarkers in the tumor microenvironment. To this end, chromogenic multiplexing immunohistochemistry (mIHC) serves as a promising solution, which enables simultaneous detection of multiple biomarkers in the same tissue sections. To automate whole-slide scoring for mIHC, a crucial analysis step involves the identification of cell locations along with their biomarker staining status (presence/absence of positive staining signals), which we call biomarker status identification. However, developing algorithms for such analysis, especially deep-learning (DL) models, often requires manual labeling at the cell-level, which is time-consuming and resource-intensive. Here, we present a DL based method to accelerate groundtruth label generation for chromogenic duplex (tissue samples stained with two biomarkers) images. We first generated approximate cell labels and then developed a DL based interactive segmentation system to efficiently refine the cell labels. Our method avoided extensive manual labeling and reduced the time of label generation to 50%-25% of manual labeling, while achieving $<$5% error rate in pathologist review.",
        "authors": "Satarupa Mukherjee, Qinle Ba, Jim Martin, Yao Nie",
        "award": "",
        "id": "23",
        "melba": "False",
        "or_id": "dI6wYt1qr1o",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S023.pdf",
        "title": "A Deep-Learning Based Approach to Accelerate Groundtruth Generation for Biomarker Status Identification in Chromogenic Duplex Images",
        "yt_full": "https://youtu.be/_uSRj0-Pqe4"
    },
    "S024": {
        "abstract": "Federated learning (FL) is a cutting-edge method for distributed learning used in many fields, including healthcare. However, medical centers need sufficient local data to train local models and participate in an FL network, which is often not feasible for rare and pediatric diseases or small hospitals with limited patient data. As a result, these centers cannot directly contribute to FL model development. To address this issue, this work explores the effectiveness of a different approach called the travelling model (TM). Specifically, this work evaluates the performances of FL and TM when only very small sample sizes are available at each center. Brain age prediction was used as an example case for comparison in this work. Our results indicate that the TM outperforms FL across all sample sizes tested, particularly when each center has only one sample.",
        "authors": "Raissa Souza, Pauline Mouches, Matthias Wilms, Anup Tuladhar, Sonke Langner, Nils Forkert",
        "award": "",
        "id": "24",
        "melba": "False",
        "or_id": "cAVR6QftKDe",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M38",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Distributed learning effectiveness in medical image analysis when trained with limited dataset",
        "yt_full": ""
    },
    "S025": {
        "abstract": "In this paper, we propose a deep learning-based method to segment the skeletal structures in the micro-CT images of Dutch-Belted rabbit fetuses which can assist in the assessment of drug-induced skeletal abnormalities as a required study in developmental and reproductive toxicology (DART). Our strategy leverages sub-optimal segmentation labels of 22 skull bones from 26 micro-CT volumes and maps them to 250 unlabeled volumes on which a deep CNN-based segmentation model is trained. In the experiments, our model was able to achieve an average Dice Similarity Coefficient (DSC) of 0.89 across all bones on the testing set, and 14 out of the 26 skull bones reached average DSC >0.93. Our next steps are segmenting the whole body followed by developing a model to classify abnormalities.",
        "authors": "Rajath Soans, Alexa Gleason, Tosha Shah, Corey Miller, Barbara Robinson, Kimberly Brannen, Antong Chen",
        "award": "",
        "id": "25",
        "melba": "False",
        "or_id": "O4f3k8zIZe9",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T38",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Deep learning-based segmentation of rabbit fetal skull with limited and sub-optimal training labels",
        "yt_full": ""
    },
    "S026": {
        "abstract": "Accurate needle placement during nerve block procedures is essential for safe and effective anesthesia and pain management. However, tracking needles and nerves in an austere setting using Point of Care Ultrasound (POCUS) can be challenging due to the complexity of the surrounding anatomy, the lack of real-time feedback and limited image quality. In this paper, we propose a method for segmenting these structures and estimating the pixelwise uncertainty using a novel approach: Temporal Monte Carlo Dropout. We demonstrate the effectiveness of our approach in POCUS with a stable probe, where it provides robust uncertainty estimates in challenging imaging scenarios while simultaneously tracking the needle accurately. Our method obtains an 84% similarity score with uncertainty estimates obtained from Monte Carlo Dropout with an 8x decrease in computational complexity without compromising segmentation performance. Importantly, it can be easily integrated into existing POCUS workflows on portable devices and has the potential to benefit medical practitioners and patients alike.  ",
        "authors": "Nishanth Thumbavanam Arun, Leonard Weiss, Andrew Schoenling, Marek Radomski, Frank Guyette, Napoleon Roux, Brittany Daley, Michael J Morris, Howie Choset, John Galeotti",
        "award": "",
        "id": "26",
        "melba": "False",
        "or_id": "WvReNPBoB9F",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W28",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Temporal Monte Carlo Dropout for Robust Uncertainty Quantification: Application to Point-of-Care Ultrasound-guided Nerve Blocks",
        "yt_full": ""
    },
    "S027": {
        "abstract": "Deep-learning based image generation methods have been widely used to overcome data deficiency.  The same is true also as in medical field, where data shortage problem is frequent. In this study, we propose multi-modal brain tumor Magnetic Resonance Imaging (MRI) generation framework, called Disentangled Latent Diffusion Model (DLDM) to tackle data deficiency in medical imaging.  We train an autoencoder that disentangles the feature of multi-modal MR images into modality-sharing and modality-specific representations.  By utilizing the feature disentanglement learned from the autoencoder, we were able to train a diffusion model that can generate modality-sharing and modality-specific latent vector.  We evaluate our approach with clean-FID and improved precision \\& recall. The results were compared with GAN-based model, StyleGAN2.",
        "authors": "Yoonho Na, Kyuri Kim, Sung-Joon Ye, Hwiyoung Kim, Jimin Lee",
        "award": "",
        "id": "27",
        "melba": "False",
        "or_id": "4HHb2cTgbO1",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S027.pdf",
        "title": "Generation of Multi-modal Brain Tumor MRIs with Disentangled Latent Diffusion Model",
        "yt_full": ""
    },
    "S028": {
        "abstract": "Performance of state-of-the-art deep learning methods is often impacted when evaluated on data coming from unseen acquisition settings, hindering their approval by the regulatory agencies and incorporation to the clinic. In recent years, several techniques have been proposed for improving the generalizability of models by using the target data and their corresponding ground truths. Some of those approaches have been adopted in histopathology, however they either focus on pixel-level predictions or simple tile level classification tasks with or without target labels. In this work, we investigate adversarial strategies in weakly supervised learning frameworks in digital pathology domain without access to the target labels, thereby strengthening the generalizability to unlabeled target domains. We evaluate several strategies on Camelyon dataset for metastatic tumor detection tasks and show that some methods can improve the average F1-score over 10% for the target domain.",
        "authors": "Javier Garcia-Baroja, Samaneh Abbasi Sureshjani, Nazim Shaikh, konstanty Korski",
        "award": "",
        "id": "28",
        "melba": "False",
        "or_id": "2cL0MFcxksh",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T39",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "A Comparative Study of Unsupervised Adversarial Domain Adaptation Strategies in Multiple-instance Learning Frameworks for Digital Pathology",
        "yt_full": ""
    },
    "S029": {
        "abstract": "The distribution and volume of fat and muscle in APCT play an important role as a biomarker. In this study, APCT data from 200 individuals who underwent health screening was labeled into three classes of fat and four classes of muscle. Based on this labeling, 3D patch-wise segmentation was performed on the whole abdomen and pelvic scan images. The test results showed an overall class average of 0.89 DSC. This study conducted 3D whole-abdomen body composition segmentation using a total of eight segmented body composition labels including the background and verified its feasibility using random patches effective for the data and task.",
        "authors": "Minyoung Kim, Ji-Won Kwon, Kwang Suk Lee, Taehoon Shin",
        "award": "",
        "id": "29",
        "melba": "False",
        "or_id": "yCx-3_76pY",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W29",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "3D Body Composition Segmentation in Abdomen and Pelvis CT using Subdivided Labels and Random Patch",
        "yt_full": ""
    },
    "S030": {
        "abstract": "We consider a novel problem setting: data-free one-shot federated regression. This setting aims to prepare a global model through a single round of communication without relying on auxiliary information, e.g., proxy datasets. To address this problem, we propose a practical framework that consists of three stages: local training, data synthesizing, and knowledge distillation, and demonstrate its efficacy with an application to bone age assessment. We conduct validation under independent and identical distribution (IID) and non-IID settings while considering both model homogeneity and heterogeneity. Validation results show that our method surpasses FedAvgOneShot by a large margin and sometimes even outperforms the proxy-data-dependent approach FedOneShot.",
        "authors": "Zhou Zheng, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori",
        "award": "",
        "id": "30",
        "melba": "False",
        "or_id": "A--Xy77jTa",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S030.pdf",
        "title": "Data-Free One-Shot Federated Regression: An Application to Bone Age Assessment",
        "yt_full": "https://youtu.be/yBLyiUdDKYw"
    },
    "S031": {
        "abstract": "Atherosclerosis is a multifactorial disease in which deposits of fat form in the arteries. These plaques can cause ischemic heart disease or other follow-up diseases. To investigate etiology and possible treatment options, mice were used as models and histological whole slide images (WSI) were obtained and analyzed. Currently, the plaque content is often estimated using a threshold-based segmentation technique, which requires a manual selection of reference points. To improve this process, an unsupervised segmentation technique is developed using the W-Net architecture. The network weights are updated using two loss functions, the soft N-cut loss, and a reconstruction loss. The update procedure of both U-networks and the weighting function in soft N-cut loss are adapted to the given task. Since no ground truth is available, the results were compared with a post-processed threshold segmentation. The evaluation showed that a linear decaying pixel distance weighting achieves the highest score. The results indicate that an unsupervised learning procedure is able to correctly identify the plaque clusters.",
        "authors": "Johann Christopher Engster, Nele Blum, Tobias Reinberger, Pascal Stagge, Thorsten Buzug, Jeanette Erdmann, Zouhair Aherrahrou, Maik Stille",
        "award": "",
        "id": "31",
        "melba": "False",
        "or_id": "a6--BnpcdB",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T40",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Unsupervised Plaque Segmentation on Whole Slide Images",
        "yt_full": ""
    },
    "S032": {
        "abstract": "The current diagnostic and outcome prediction methods for pancreatic cancer lack prognostic power. As such, identifying novel biomarkers using machine learning has become of increasing interest. In this study, we introduce a novel method for estimating the tumor-stroma ratio (TSR) in whole slide images (WSIs) of pancreatic tissue and assess its potential as a prognostic biomarker. A multi-step strategy for estimating TSR is proposed, including epithelium segmentation based on an immunohistochemical reference standard, a coarse pancreatic cancer segmentation, and a post-processing pipeline for TSR quantification. The resultant segmentation models are validated on external test sets using the Dice coefficient, and additionally, the TSR's potential as a prognostic factor is assessed using survival analysis, resulting in a C-index of 0.61.",
        "authors": "Pierpaolo Vendittelli, JM Bokhorst, Esther Markus Smeets, Valentyna Kryklyva, Lodewijk Brosens, Caroline Verbeke, Geert Litjens",
        "award": "",
        "id": "32",
        "melba": "False",
        "or_id": "Dtz_iaUpGc",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W30",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Automatic quantification of TSR as a prognostic marker for pancreatic cancer.",
        "yt_full": ""
    },
    "S033": {
        "abstract": "Expert guidelines standardize the delineation of the clinical target volume by giving neighboring anatomical structures as boundaries. In this research, we have automated the segmentation of seventy-one most mentioned anatomical structures in the expert guidelines. For most structures there are no segmentation accuracies found in the literature. For others, the DICE improves in our research. The sDICE with a 2 mm tolerance shows clinical acceptance for most structures. Overall, we set benchmarks for several structures of the head and neck anatomy. With these segmentations the guideline-conformance of clinical target volumes can be measured.",
        "authors": "Alexandra Walter, Goran Stanic, Philipp Hoegen, Sebastian Adeberg, Oliver Jaekel, Martin Frank, Kristina Giske",
        "award": "",
        "id": "33",
        "melba": "False",
        "or_id": "hhCvgZQQbQ5",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M39",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Segmentation of seventy-one anatomical structures necessary for the evaluation of guideline-conform clinical target volumes in head and neck cancers",
        "yt_full": ""
    },
    "S035": {
        "abstract": "In histopathology, histologic elements are not randomly located across an image but organize into structured patterns. In this regard, classification tasks or feature extraction from histology images may require context information to increase performance. In this work, we explore the importance of keeping context information for a cell classification task on Hematoxylin and Eosin (H$\\&$E) scanned whole slide images (WSI) in colorectal cancer. We show that to differentiate normal from malignant epithelial cells, the environment around the cell plays a critical role. We propose here an image augmentation based on gamma variations to guide deep learning models to focus on the object of interest while keeping context information. This augmentation method yielded more specific models and helped to increase the model performance (weighted F1 score with/without gamma augmentation respectively, PanNuke: 99.49 vs 99.37 and TCGA: 91.38 vs. 89.12, $p<0.05$). ",
        "authors": "Ana Leni Frei, Amjad Khan, Philipp Zens, Alessandro Lugli, Inti Zlobec, Andreas Fischer",
        "award": "",
        "id": "35",
        "melba": "False",
        "or_id": "MCAgRjgh6v",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T41",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "GammaFocus: An image augmentation method to focus model attention for classification",
        "yt_full": ""
    },
    "S036": {
        "abstract": "In digital pathology, cell-level tissue analyses are widely used to better understand tissue composition and structure. Publicly available datasets and models for cell detection and classification in colorectal cancer exist but lack the differentiation of normal and malignant epithelial cells that are important to perform prior to any downstream cell-based analysis. This classification task is particularly difficult due to the high intra-class variability of neoplastic cells. To tackle this, we present here a new method that uses graph-based node classification to take advantage of both local cell features and global tissue architecture to perform accurate epithelial cell classification. The proposed method demonstrated excellent performance on F1 score (PanNuke: 1.0, TCGA: 0.98) and performed significantly better than conventional computer vision methods (PanNuke: 0.99, TCGA: 0.92).",
        "authors": "Ana Leni Frei, Amjad Khan, Linda Studer, Philipp Zens, Alessandro Lugli, Andreas Fischer, Inti Zlobec",
        "award": "",
        "id": "36",
        "melba": "False",
        "or_id": "HlkroJOY-J",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W31",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Local and global feature aggregation for accurate epithelial cell classification using graph attention mechanisms in histopathology images",
        "yt_full": ""
    },
    "S038": {
        "abstract": "Non-invasive arterial spin labeling is an MRI technique that can be used for kidney transplant evaluation and perfusion estimation. This work proposes an automatic workflow for renal segmentation and perfusion estimation, based on deep learning approach and image analysis. Similarity coefficients above 90\\% are achieved for kidney, cortex, and medulla segmentation results and perfusion values within the acceptable ranges are obtained. ",
        "authors": "Anne Oyarzun-Dome\u00f1o, Izaskun C\u00eda, Rebeca Echeverria-Chasco, Mar\u00eda A Fern\u00e1ndez-Seara, Paloma L. Martin-Moreno, Nuria Garcia-Fernandez, Gorka Bastarrika, Javier Navallas, Arantxa Villanueva",
        "award": "",
        "id": "38",
        "melba": "False",
        "or_id": "Z376GMHarUB",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M40",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Automatic renal perfusion estimation on postoperative PCASL MRI based on deep learning image analysis and segmentation ",
        "yt_full": ""
    },
    "S039": {
        "abstract": "Equivariance in convolutional neural networks (CNN) has been a long-sought property, as it would ensure robustness to expected effects in the data. Convolutional filters are by nature translation-equivariant, and rotation-equivariant kernels were proposed recently. While these filters can be paired with learnable weights to form equivariant networks (E-CNN), we show here that such E-CNNs have a limited learning capacity, which makes them fragile against even slight changes in intensity distribution. This sensitivity to intensity changes presents a major challenge in medical imaging where many noise sources can randomly corrupt the data, even for consecutive scans of the same subject. Here, we propose a hybrid architecture that successively decouples intensity and spatial features: we first remove irrelevant noise in the data with a denoising CNN, and then use an E-CNN to extract robust spatial features. We demonstrate our method for motion tracking in fetal brain MRI, where it considerably outperforms standard CNNs and E-CNNs.",
        "authors": "Benjamin Billot, Daniel Moyer, Neerav Karani, Malte Hoffmann, Esra Abaci Turk, Ellen Grant, Polina Golland",
        "award": "",
        "id": "39",
        "melba": "False",
        "or_id": "C7VKeiHeZT",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T42",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Equivariant and Denoising CNNs to Decouple Intensity and Spatial Features for Motion Tracking in Fetal Brain MRI",
        "yt_full": ""
    },
    "S042": {
        "abstract": "Automatic high-quality segmentations have become ubiquitous in numerous downstream tasks of medical image analysis, i.e. shape-based pathology classification or semantically guided image registration. Public frameworks for 3D U-Nets provide numerous pre-trained models for nearly all anatomies in CT scans. Yet, the great generalisation comes at the cost of very heavy networks with millions of parameter and trillions of floating point operations for every single model in even larger ensembles. We present a novel combination of two orthogonal approaches to lower the computational (and environmental) burden of U-Nets: namely partial convolution and structural re-parameterization that tackle the intertwined challenges while keeping real world latency small.",
        "authors": "Mattias P Heinrich",
        "award": "",
        "id": "42",
        "melba": "False",
        "or_id": "l_Zxaj0PZE",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W51",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Make nnUNets Small Again",
        "yt_full": ""
    },
    "S043": {
        "abstract": "High-throughput 3D nuclei instance segmentation (NIS) is critical to understanding the complex structure and function of individual cells and their interactions within the larger tissue environment in the brain. Despite the significant progress in achieving accurate NIS within small image stacks using cutting-edge machine learning techniques, there has been a lack of effort to extend this approach towards whole-brain NIS. To address this challenge, we propose an efficient deep stitching neural network built upon a knowledge graph model characterizing 3D contextual relationships between nuclei. Our deep stitching model is designed to be agnostic, enabling existing limited methods (optimized for image stack only) to overcome the challenges of whole-brain NIS, particularly in addressing the issue of inter- and intra-slice gaps. We have evaluated the NIS accuracy on top of state-of-the-art deep models, such as Cellpose, with $128\\times 128\\times 64$ image stacks.",
        "authors": "Ziquan Wei, Guorong Wu",
        "award": "",
        "id": "43",
        "melba": "False",
        "or_id": "_cft_bbodYO",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M41",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "A General Stitching Solution for Whole-Brain 3D Nuclei Instance Segmentation from Microscopy Images",
        "yt_full": ""
    },
    "S044": {
        "abstract": "Computed tomography (CT) can provide quantitative body composition metrics of tissue volume, morphology, and quality which are valuable for disease prediction and prognostication. However, manually extracting these measures is a cumbersome and time-consuming task. Proprietary software to automate this process exist, but these software are closed-source, impeding large-scale access to and usage of these tools. To address this, we have built Comp2Comp, an open-source Python package for rapid and automated body composition analysis of CT scans. The primary advantages of Comp2Comp are its open-source nature, the inclusion of multiple tissue analysis capabilities within a single package, and its extensible design. We discuss the architecture of Comp2Comp and report initial validation results. Comp2Comp can be found at https://github.com/StanfordMIMI/Comp2Comp.",
        "authors": "Louis Blankemeier, Malte Jensen, Eduardo Pontes Reis, Juan Manuel Zambrano Chaves, Adrit Rao, Sally Yao, Pauline Margaret Berens, Andrew Wentland, Bhanushree Bahl, Kushboo Arora, Oliver Oppers Aalami, Bhavik Patel, Leon Lenchik, Marc H. Willis, Robert D. Boutin, Arjun D Desai, Akshay S Chaudhari",
        "award": "",
        "id": "44",
        "melba": "False",
        "or_id": "VcgBBAQfMP",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "",
        "title": "Comp2Comp: Open-Source Body Composition Assessment on Computed Tomography",
        "yt_full": ""
    },
    "S045": {
        "abstract": "The reliability of deep learning based segmentation models is essential to the safe translation of these models into clinical practise. Unfortunately, these models are sensitive to distributional shifts. This is particularly notable in MRI, where there is a large variation of acquisition protocols across different domains leading to varying textural profiles. We hypothesise that the constrained anatomical variability across subjects can be leveraged to discretize the latent space to a dictionary of shape components. We achieve this by using multiple MRI sequences to learn texture invariant and shape equivariant features which are used to construct a shape dictionary using vector quantisation. This dictionary is then sampled to compose the segmentation output. Our method achieves SOTA performance in the task of single domain generalisation (SDG) for prostate zonal segmentation.",
        "authors": "Ainkaran Santhirasekaram, Mathias Winkler, Andrea G. Rockall, Ben Glocker",
        "award": "",
        "id": "45",
        "melba": "False",
        "or_id": "TyA5AyU_tSv",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "",
        "title": "Shape Equivariant Learning for Robust MRI Segmentation",
        "yt_full": "https://youtu.be/3UyFKHH6_Yg"
    },
    "S049": {
        "abstract": "Active learning (AL) selects informative samples for annotation. This is becoming increasingly crucial to medical image segmentation since image annotation is hardly scalable to full pixel-level labeling of large datasets. However, most research focuses on classification or natural image segmentation. Uncertainty-based AL methods tend to have sub-optimal batch-query strategies, and diversity-based methods are computationally expensive. This work improves uncertainty-based AL for medical image segmentation using stochastic batches during sampling, computing uncertainty at the batch-level. Experiments on MRI prostate imaging show this approach\u2019s effectiveness and robustness under various conditions.",
        "authors": "M\u00e9lanie Gaillochet, Christian Desrosiers, Herve Lombaert",
        "award": "",
        "id": "49",
        "melba": "False",
        "or_id": "upQogJXuhQ",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M42",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Active learning for medical image segmentation with stochastic batches",
        "yt_full": ""
    },
    "S050": {
        "abstract": "Lung cancer has been one of the leading causes of cancer-related deaths worldwide for years. With the emergence of deep learning, computer-assisted diagnosis (CAD) models based on learning algorithms can accelerate the nodule screening process, providing valuable assis- tance to radiologists in their daily clinical workflows. However, developing such robust and accurate models often requires large-scale and diverse medical datasets with high-quality annotations. Generating synthetic data provides a pathway for augmenting datasets at a larger scale. Therefore, in this paper, we explore the use of Semantic Diffusion Mod- els (SDM) to generate high-fidelity pulmonary CT images from segmentation maps. We utilize annotation information from the LUNA16 dataset to create paired CT images and masks, and assess the quality of the generated images using the Fr \u0301echet Inception Distance (FID), as well as on two common clinical downstream tasks: nodule detection and nodule localization. Achieving improvements of 3.953% for detection accuracy and 8.5% for AP50 in nodule localization task, respectively, demonstrates the feasibility of the approach.",
        "authors": "Xuan Zhao, Benjamin Hou",
        "award": "",
        "id": "50",
        "melba": "False",
        "or_id": "2M-2-75emE",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S050.pdf",
        "title": "High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using Semantic Diffusion Model",
        "yt_full": "https://youtu.be/06peLTLnmWg"
    },
    "S053": {
        "abstract": "We propose a multi-hop graph attention module (MGA) that addresses the limitation of CNN in capturing non-local connections of features for predicting brain age. MGA converts feature maps to graphs, calculates distance-based scores, and uses Markov property and graph attention to capture direct and indirect connectivity. Combining MGA with sSE-ResNet18, we achieved a mean absolute error (MAE) of 2.822 years and Pearson's correlation coefficient (PCC) of 0.968 using 2,788 T1-weighted MR images of healthy subjects. Our results present a possibility of MGA as a new algorithm for brain age prediction.",
        "authors": "Heejoo Lim, Yoonji Joo, Eunji Ha, Yumi Song, Sujung Yoon, In Kyoon Lyoo, Taehoon Shin",
        "award": "",
        "id": "53",
        "melba": "False",
        "or_id": "brK-VVoDpqo",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W33",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Brain age prediction using multi-hop graph attention module(MGA) with convolutional neural network",
        "yt_full": ""
    },
    "S055": {
        "abstract": "Contrastive representation learning techniques trained on large multi-modal datasets, such as CLIP and CLOOB, have demonstrated impressive capabilities of producing highly transferable representations for different downstream tasks. In the field of ophthalmology, large multi-modal datasets are conveniently accessible as retinal imaging scanners acquire both 2D fundus images and 3D optical coherence tomography to evaluate the disease. Motivated by this, we propose a CLIP/CLOOB objective-based model to learn joint representations of the two retinal imaging modalities. We evaluate our model's capability to accurately retrieve the appropriate OCT based on a fundus image belonging to the same eye. Furthermore, we showcase the transferability of the obtained representations by conducting linear probing and fine-tuning on several prediction tasks from OCT.",
        "authors": "Emese S\u00fckei, Elisabeth Rumetshofer, Niklas Schmidinger, Ursula Schmidt-Erfurth, G\u00fcnter Klambauer, Hrvoje Bogunovi\u0107",
        "award": "",
        "id": "55",
        "melba": "False",
        "or_id": "newlahoISt1",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M43",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\nWednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S055.pdf",
        "title": "Learning Retinal Representations from Multi-modal Imaging via Contrastive Pre-training",
        "yt_full": "https://youtu.be/8anDKyJuGe0"
    },
    "S056": {
        "abstract": "White matter hyperintensities (WMH) are associated with an increased risk of stroke, cognitive decline, and dementia. A robust, yet accurate detection of WMH can help with the prevention of more lesions from forming. The task is still challenging as the lesions are often small and irregular. Hence, we propose a robust deep learning-based method for the automatic segmentation of WMH only using fluid-attenuated inversion recovery (FLAIR) scans and MRI-specific data augmentation and compare it with state-of-the-art methods. The methods are tested on public and private data, and we show that our model is more robust to domain shift and achieves higher segmentation accuracy than the alternatives.",
        "authors": "Alice Schiavone, Sebastian N\u00f8rgaard Llambias, Jacob Johansen, Silvia Ingala, Akshay Pai, Mads Nielsen, Mostafa Mehdipour Ghazi",
        "award": "",
        "id": "56",
        "melba": "False",
        "or_id": "c0KnufAuX6k",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T43",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Robust Identification of White Matter Hyperintensities in Uncontrolled Settings Using Deep Learning",
        "yt_full": ""
    },
    "S059": {
        "abstract": "Foundation models have taken over natural language processing and image generation domains due to the flexibility of prompting. With the recent introduction of the Segment Anything Model (SAM), this prompt-driven paradigm has entered image segmentation with a hitherto unexplored abundance of capabilities. The purpose of this paper is to conduct an initial evaluation of the out-of-the-box zero-shot capabilities of SAM for medical image segmentation, by evaluating its performance on an abdominal CT organ segmentation task, via point or bounding box based prompting.  We show that SAM generalizes well to CT data, making it a potential catalyst for the advancement of semi-automatic segmentation tools for clinicians.  We believe that this foundation model, while not reaching state-of-the-art segmentation performance in our investigations, can serve as a highly potent starting point for further adaptations of such models to the intricacies of the medical domain.",
        "authors": "Tassilo Wald, Saikat Roy, Gregor Koehler, Nico Disch, Maximilian Rouven Rokuss, Julius Holzschuh, David Zimmerer, Klaus Maier-Hein",
        "award": "",
        "id": "59",
        "melba": "False",
        "or_id": "iilLHaINUW",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W49",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model",
        "yt_full": ""
    },
    "S062": {
        "abstract": "Fully-supervised machine learning has been established as an effective method for medical image segmentation. However, it requires large amounts of expert-annotated data, which can be a bottleneck in certain applications. Unsupervised methods like anomaly localization have proven their potential without relying on any labeled data, making them potentially much more scalable than fully supervised methods. Despite their scalability advantages,  unsupervised and self-supervised methods have not yet fully reached the performance level of fully supervised models. As a first step to close this gap, we propose an approach that combines both concepts. We fine-tune a pre-trained anomaly localization model, namely a self-supervised denoising auto-encoder, using varying amounts of labeled training data in a supervised manner. Overall this approach exhibits superior performance compared to a model trained from scratch, especially in a low labeled training data regime.",
        "authors": "Julius Holzschuh, David Zimmerer, Constantin Ulrich, Michael Baumgartner, Gregor Koehler, Rainer Stiefelhagen, Klaus Maier-Hein",
        "award": "",
        "id": "62",
        "melba": "False",
        "or_id": "OytzS_LCWvw",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M44",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Combining Anomaly Detection and Supervised Learning for Medical Image Segmentation",
        "yt_full": ""
    },
    "S067": {
        "abstract": "Self-supervised training minimizes a contrastive loss objective for unlabeled data. Contrastive loss estimates the distance in the latent space between positive pairs, which are pairs of images that are expected to have the same label. For medical images, choosing positive pairs is challenging because simple transformations like rotations or blurs are not class-invariant. In this paper, we show that choosing positive pairs with nearest-neighbor radiomics features for self-supervised training improves chest X-ray pneumonia identification accuracy by 8.4% without labeled data.",
        "authors": "Cailin Winston, Caleb Winston, Chloe Winston",
        "award": "",
        "id": "67",
        "melba": "False",
        "or_id": "rUpjCWd0BB",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T44",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\nWednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "",
        "title": "Nearest Neighbor Radiomics for Self-Supervised Chest X-ray Pneumonia Identification",
        "yt_full": ""
    },
    "S069": {
        "abstract": "Curation of large-scale annotated clinical data for training could be challenging due to scarcity or ethical issues. As an alternative, synthetically generated data can be used to train network for recognising basic features. In this work, we propose the novel training scheme using synthetic chest X-rays generated from 3D photon-counting CT volumes for quantifying the internal patient rotation $\\alpha$. This can automatically inform the technician if and how re-exposure is needed without the need of extensive image analysis. X-ray images were forward projected with a step size of 2$\\degree$ rotation along patient axis. 1167 images and labels were trained on a modified DenseNet-121 to detect $\\alpha$. Results on 252 test images showed good correlation between true and predicted $\\alpha$, with $R^2$= 0.992, with 95% confidence level of $\\approx \\pm$2$\\degree $. ",
        "authors": "Wai Yan Ryana Fok, Andreas Fieselmann, Magdalena Herbst, Dominik Eckert, Marcel Beister, Steffen Kappler, Sylvia Saalfeld",
        "award": "",
        "id": "69",
        "melba": "False",
        "or_id": "rE5kyC31IXQ",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S069.pdf",
        "title": "Learning Patient Rotation Using Synthetic X-ray Images from 3D CT Volumes",
        "yt_full": "https://youtu.be/PuUKcUmDilE"
    },
    "S071": {
        "abstract": "We present an interactive detection model to improve the cell annotation workflow of diffuse gastric cancer. The model relates image and user inputs and is trained to detect three types of cells in diffuse gastric cancer histology. We measure model multi-class cell detection performance as per-class F1 score and we show that it increases with the number of user input clicks. Moreover, we show that the proposed interactive annotation approach substantially reduces the number of required user actions needed for complete image annotation, achieving a 17\\% reduction for the multi-class case. Future work will implement an iterative approach to filter out recurring false positives for further performance improvement.",
        "authors": "Robin Lomans, Rachel van der Post, Francesco Ciompi",
        "award": "",
        "id": "71",
        "melba": "False",
        "or_id": "J06Ap1NYWE",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W32",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Interactive Cell Detection in H&E-stained slides of Diffuse Gastric Cancer",
        "yt_full": ""
    },
    "S072": {
        "abstract": "Anatomically consistent field-of-view (FOV) completion to recover truncated body sections has important applications in quantitative analyses of computed tomography (CT) with limited FOV. Existing solution based on conditional generative models relies on the fidelity of synthetic truncation patterns at training phase, which poses limitations for the generalizability of the method to potential unknown types of truncation. In this study, we evaluate a zero-shot method based on a pretrained unconditional generative diffusion prior, where truncation pattern with arbitrary forms can be specified at inference phase. In evaluation on simulated chest CT slices with synthetic FOV truncation, the method is capable of recovering anatomically consistent body sections and subcutaneous adipose tissue measurement error caused by FOV truncation. However, the correction accuracy is inferior to the conditionally trained counterpart.",
        "authors": "Kaiwen Xu, Aravind Krishnan, Thomas Z. Li, Yuankai Huo, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman",
        "award": "",
        "id": "72",
        "melba": "False",
        "or_id": "hr1QLA_ykVp",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T45",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior",
        "yt_full": ""
    },
    "S073": {
        "abstract": "Ovarian cancer ranks the $5^{th}$ in cancer deaths among women, accounting for more deaths than any other cancer of the female reproductive system.  We propose a 3D contrastive learning based predictive model to discriminate benign from malignant masses in abdominal CT scans for ovarian cancer patients. We used fully supervised contrastive learning(SCL) approach which allowed us to effectively leverage the label information of our small dataset of 331 patients. All patients' data was collected at the University of Iowa. Three different architectures (VGG, ResNet and DenseNet) were implemented for  feature extraction by contrastive learning. We showed that SCL consistently out-performed over the traditional cross-entropy based networks with VGG and two ResNet variants. With five fold cross validation, our best contrastive learning model achieves an accuracy of 92.8\\%, mean AUC of 92.4\\%, mean recall of 94.45\\% and mean specificity of 90.37\\%.  This work shows that contrastive learning is a promising deep learning method to improve early detection of women at risk of harboring ovarian cancer.",
        "authors": "Tarun Kanti Roy, Suely Oliveira, Jesus Gonzalez Bosquet, Xiaodong Wu",
        "award": "",
        "id": "73",
        "melba": "False",
        "or_id": "BC4UYzbLRZ",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S073.pdf",
        "title": "3D Supervised Contrastive-Learning Network for Classification of Ovarian Neoplasms",
        "yt_full": "https://youtu.be/0172QwDwr_U"
    },
    "S076": {
        "abstract": "Diffusion models have recently delivered state-of-the-art performance for MRI reconstruction with improved robustness. However, these models still fail when there is a large distribution shift, and their long inference times impede their clinical utility. In this paper, we present regularization by denoising diffusion processes for MRI reconstruction (RED-diff). RED-diff formulates sampling as stochastic optimization, and outperforms diffusion baselines in PSNR/SSIM with 3x faster inference while using the same amount of memory.",
        "authors": "Batu Ozturkler, Morteza Mardani, Arash Vahdat, Jan Kautz, John M. Pauly",
        "award": "",
        "id": "76",
        "melba": "False",
        "or_id": "M1V498MXelq",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T46",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Regularization by Denoising Diffusion Process for MRI Reconstruction",
        "yt_full": ""
    },
    "S077": {
        "abstract": "In recent years, Multiple Instance Learning (MIL) approaches have gained popularity to address the task of weakly-supervised tumor detection in whole-slide images (WSIs). However, standard MIL relies on classification methods for tumor detection that require negative control, i.e., tumor-free cases, which are challenging to obtain in real-world clinical scenarios, especially when considering surgical resection specimens. Inspired by recent work, in this paper we tackle tumor detection via a MIL-like weakly-supervised regression approach to predict the percentage of tumor present in WSIs, a clinically available target that allows to overcome the problem of need for manual annotations or presence of tumor-free slides. We characterize the quality of such a target by investigating its robustness in the presence of noise on regression percentages and provide explainability through attention maps. We test our approach on breast cancer data from primary tumor and lymph node metastases.",
        "authors": "Marina D'Amato, Maschenka Balkenhol, Mart van Rijthoven, Jeroen van der Laak, Francesco Ciompi",
        "award": "",
        "id": "77",
        "melba": "False",
        "or_id": "npxsTyiJ37",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W36",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "On the robustness of regressing tumor percentage as an explainable detector in histopathology whole-slide images",
        "yt_full": ""
    },
    "S080": {
        "abstract": "The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical image analysis, especially for digital pathology where the training data are rare. In this study, we evaluate the zero-shot segmentation performance of SAM model on representative segmentation tasks on whole slide imaging (WSI), including (1) tumor segmentation, (2) non-tumor tissue segmentation, (3) cell nuclei segmentation. Core Results: The results suggest that the zero-shot SAM model achieves remarkable segmentation performance for large connected objects. However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image. We also summarized the identified limitations for digital pathology: (1) image resolution, (2) multiple scales, (3) prompt selection, and (4) model fine-tuning. In the future, the few-shot fine-tuning with images from downstream pathological segmentation tasks might help the model to achieve better performance in dense object segmentation. ",
        "authors": "Ruining Deng, Can Cui, Quan Liu, Tianyuan Yao, Lucas Walker Remedios, Shunxing Bao, Bennett A. Landman, Yucheng Tang, Lee E. Wheless, Lori A. Coburn, Keith T. Wilson, Yaohong Wang, Agnes B. Fogo, Haichun Yang, Yuankai Huo",
        "award": "",
        "id": "80",
        "melba": "False",
        "or_id": "lUZGyTRzxq",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M46",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging",
        "yt_full": ""
    },
    "S081": {
        "abstract": "Blue light cystoscopy (BLC) has been shown to detect bladder tumors with better sensitivity than white light cystoscopy (WLC); however, its increased cost and dye administration time have challenged widespread adoption of the technology. Here, we demonstrate a low-cost strategy to generate BLC images directly from WLC images. We performed digital staining of WLC images obtained from tumor resection procedures and demonstrate that the resulting digitally generated BLC images show strong resemblance to ground truth BLC images, with negligible degradation of the image quality.",
        "authors": "Shuang Chang, Haoli Yin, Kristen R Scarpato, Amy N Luckenbaugh, Sam Chang, Christian Bolenz, Maximilian C Kriegmair, Nikolaos C Deliolanis, Soheil Kolouri, Audrey Bowden",
        "award": "",
        "id": "81",
        "melba": "False",
        "or_id": "fnIAVuZa9J",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T47",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Digital Staining of Unpaired White and Blue Light Cystoscopy Videos for Bladder Cancer Detection in the Clinic",
        "yt_full": ""
    },
    "S082": {
        "abstract": "Deep Learning methods over the past years provided high-performance solutions for the medical applications. Yet, robustness and quality control is still required for clinical applicability. In this work, the uncertainty of proximal femur fracture classification,was modeled. We introduce a reliability measure to our predictive model using the Monte Carlo Dropout approach. We performed an extensive quantitative and qualitative analysis to validate the results. We further exposed the results to expert physicians in order to get feedback on the model\u2019s performance and uncertainty measures. Results demonstrate a positive correlation between the miss-classification of the model\u2019s prediction and high uncertainty scores. Additionally, the uncertainty measures are mimicking the actual radiologist\u2019s uncertainty for challenging examples reflected on intra- and inter- experts variability.",
        "authors": "Selina Frenner, Mayar Lotfy, Marc Beirer, Peter Biberthaler, Shadi Albarqouni",
        "award": "",
        "id": "82",
        "melba": "False",
        "or_id": "kvpAErerdkc",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S082.pdf",
        "title": "Uncertainty for Proximal Femur Fractures Classification",
        "yt_full": "https://youtu.be/izOwvBHzejA"
    },
    "S083": {
        "abstract": "  While several methods have been proposed for automated assessment of breast-cancer response to  neoadjuvant chemotherapy on breast MRI, limited information is available about their performance across multiple institutions. In this paper, we  assess the value and robustness of nnU-Net-derived volumes of locally advanced breast cancer (LABC) on MRI to infer the presence of residual disease after neoadjuvant chemotherapy. An nnU-Net was trained to segment LABC on a single-institution training set and validated on a multi-center independent testing cohort. Based on resulting tumor volumes, an extremely randomized tree model was trained to assess residual cancer burden (RCB)-0/I vs. RCB-II/III. An independent model was developed using functional tumor volume (FTV) for comparison to an established method. Models were tested on an independent testing cohort, response assessment performance and robustness across multiple institutions were assessed.Results show that nnU-Net accurately estimate changes in tumor load on DCE-MRI, that these changes associated with RCB after NAC, and that they are robust against variations between institutions.",
        "authors": "Mark Janse, Liselore Maria Janssen, Bas H.M. van der Velden, Maaike Moman, Elian Wolters-van der Ben, Marc Kock, Max A. Viergever, Paul van Diest, Kenneth Gilhuijs",
        "award": "",
        "id": "83",
        "melba": "False",
        "or_id": "NZu3TrXvCk",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M47",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden: A Multi-Institutional Cohort Study",
        "yt_full": ""
    },
    "S084": {
        "abstract": "Prenatal ultrasound imaging is the first-choice modality to assess fetal health. Medical image datasets for AI and ML methods must be diverse (i.e. diagnoses, diseases, pathologies, scanners, demographics, etc), however there are few public ultrasound fetal imaging datasets due to insufficient amounts of clinical data, patient privacy, rare occurrence of abnormalities in general practice, and limited experts for data collection and validation. To address such data scarcity, we proposed generative adversarial networks (GAN)-based models, diffusion-super-resolution-GAN and transformer-based-GAN, to synthesise images of fetal ultrasound brain planes from one public dataset. We reported that GAN-based methods can generate 256x256 pixel size of fetal ultrasound trans-cerebellum brain image plane with stable training losses, resulting in lower FID values for diffusion-super-resolution-GAN (average 7.04 and lower FID 5.09 at epoch 10) than the FID values of transformer-based-GAN (average 36.02 and lower 28.93 at epoch 60). The results of this work illustrate the potential of GAN-based methods to synthesise realistic high-resolution ultrasound images, leading to future work with other fetal brain planes, anatomies, devices and the need of a pool of experts to evaluate synthesised images. Code, data and other resources to reproduce this work are available at \\url{https://github.com/budai4medtech/midl2023}.",
        "authors": "Michelle Iskandar, Harvey Mannering, Zhanxiang Sun, Jacqueline Matthew, Hamideh Kerdegari, Laura Peralta, Miguel Xochicale",
        "award": "",
        "id": "84",
        "melba": "False",
        "or_id": "mad9Y_7khs",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T48",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\nWednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "",
        "title": "Towards Realistic Ultrasound Fetal Brain Imaging Synthesis",
        "yt_full": ""
    },
    "S085": {
        "abstract": "The recent advent of foundation models and large language models has enabled scientists to leverage large-scale knowledge of pretrained (vision) transformers and efficiently tailor it to downstream tasks. This technology can potentially automate multiple aspects of cancer diagnosis in digital pathology, from whole-slide image classification to generating pathology reports while training with pairs of images and text from the diagnostic conclusion. In this work, we orchestrate a set of weakly-supervised transformer-based models with a first aim to address both whole-slide image classification and captioning, addressing the automatic generation of the conclusion of pathology reports in the form of image captions. We report our first results on a multicentric multilingual dataset of colon polyps and biopsies. We achieve high diagnostic accuracy with no supervision and cheap computational adaptation.",
        "authors": "Bryan Cardenas Guevara, Niccol\u00f2 Marini, Stefano Marchesin, Witali Aswolinskiy, Robert-Jan Schlimbach, Damian Podareanu, Francesco Ciompi",
        "award": "",
        "id": "85",
        "melba": "False",
        "or_id": "gpsfGAOUs58",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S085.pdf",
        "title": "Caption generation from histopathology whole-slide images using pre-trained transformers",
        "yt_full": "https://youtu.be/NNQlClrNWkg"
    },
    "S087": {
        "abstract": "Neural networks are considered black boxes. Deploying them into the healthcare domain poses a challenge in understanding model behavior beyond final prediction. There have been recent attempts to establish the trustworthiness of a model. Concept learning models provide insight into the model by introducing a bottleneck layer before the final prediction. They encourage interpretable insights into deep learning models by conditioning final predictions on intermediate predictions of explainable high-level concepts. However, using concept-based models causes a drop in performance which poses an accuracy vs explainability trade-off. To overcome this challenge we propose Coop-CBM, a novel concept learning model. We validate the performance of Coop-CBM on diverse dermatology and histopathology images. ",
        "authors": "Ivaxi Sheth, Samira Ebrahimi Kahou",
        "award": "",
        "id": "87",
        "melba": "False",
        "or_id": "BSf6JALJoc",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S087.pdf",
        "title": "Overcoming Interpretability and Accuracy Trade-off in Medical Imaging",
        "yt_full": "https://youtu.be/oYUIteM6cz0"
    },
    "S088": {
        "abstract": "Velopharyngeal dysfunction (VPD) results in speech, resonance, and swallowing difficulties due to inadequate separation of oral and nasal cavities by the velopharyngeal musculature. Diagnosing and treating VPD often involve multidisciplinary evaluation and specialized imaging techniques like videofluoroscopy or nasendoscopy. However, recent MRI applications have enabled non-invasive visualization of the vocal tract and VP mechanism, providing insights into the shape, size, movement, and position. In order to obtain this data, however, manual techniques are necessary, and analyses of 3D MRI data are time-consuming and not yet clinically feasible. This article aims to explore the feasibility of 3D medical image deep learning methods for segmenting soft palate, levator muscle, pharyngeal wall, and adenoids in the velopharyngeal region, overcoming current limitations and contributing to future clinical translation of this assessment methodology.",
        "authors": "Jiebei Liu, Donald E Brown, Kazlin Mason, Stephen Baek",
        "award": "",
        "id": "88",
        "melba": "False",
        "or_id": "OaG7pYqbs7",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T49",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Assessing Deep Learning Methodologies for Automatic Segmentation of the Velopharyngeal Mechanism",
        "yt_full": ""
    },
    "S090": {
        "abstract": "Deep learning has made major strides in medical imaging segmentation in the last several years for its automated feature extraction. This model fitting process is susceptible to over-fitting, and can benefit from sparsity. Here, we show theoretical and experimental potential of using low-entropy images as sparse input to improve deep learning driven tissue segmentation, using tumor and heart segmentation problems as exemplary cases.",
        "authors": "Daniel A Di Giovanni, Pierrick Coupe, Danilo Bzdok, D. Louis Collins",
        "award": "",
        "id": "90",
        "melba": "False",
        "or_id": "rfZokeg6UMV",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M48",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Image Entropy and Numeric Representation for MRI Semantic Segmentation",
        "yt_full": ""
    },
    "S091": {
        "abstract": "The soft-Dice loss is a very popular loss for image semantic segmentation in the medical field, and is often combined with the cross-entropy loss. It has recently been shown that the gradient of the dice loss is a \u201cnegative\u201d of the ground truth, and its supervision can be trivially mimicked by multiplying the predicted probabilities with a pre-computed \u201cgradient-map\u201d (Kervadec and de Bruijne, 2023). In this short paper, we study the properties of the dice loss, and two of its variants (Milletari et al., 2016a; Sudre et al., 2017b) when sub-patching is required, and no foreground is present. As theory and experiments show, this introduce divisions by zero which are difficult to handle gracefully while maintaining good performances. On the contrary, the mime loss of (Kervadec and de Bruijne, 2023) proved to be far more suited for sub-patching and handling of empty patches.",
        "authors": "Hoel Kervadec, Marleen de Bruijne",
        "award": "",
        "id": "91",
        "melba": "False",
        "or_id": "d1O5xjKX_yd",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W20",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "On the dice loss variants and sub-patching",
        "yt_full": ""
    },
    "S092": {
        "abstract": "Melanoma is a dangerous form of skin cancer caused by the abnormal growth of skin cells. Early diagnosis is critical to effective treatment, and several Fully Convolutional Network (FCN) approaches, including the U-Net architecture, can automatically segment skin lesions to aid diagnosis. The symmetrical U-Net model has shown outstanding results, but its use of a convolutional operation limits its ability to capture long-range dependencies, which are essential for accurate medical image segmentation. In addition, the U-shaped structure suffers from the semantic gaps between the encoder and decoder. In this study, we developed and evaluated a U-shaped hierarchical Transformer-based structure for skin lesion segmentation while we proposed an Inter-scale Context Fusion (ISCF) to utilize the attention correlations in each stage of the encoder to adaptively combine the contexts coming from each stage to hinder the semantic gaps. The preliminary results of the skin lesion segmentation benchmark endorse the applicability and efficacy of the ISCF module.",
        "authors": "Sania Eskandari, Janet Lumpp",
        "award": "",
        "id": "92",
        "melba": "False",
        "or_id": "JExQEfV5um",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W37",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Inter-Scale Dependency Modeling for Skin Lesion Segmentation with Transformer-based Networks",
        "yt_full": ""
    },
    "S093": {
        "abstract": "Deep-learning models have achieved unprecedented performance in fundamental computational tasks in digital pathology (DP) based analysis, such as image classification, cell detection and tissue segmentation. However, such models are known to suffer from catastrophic forgetting when adapted to unseen data distribution with transfer learning. With an increasing need for deep-learning models to handle ever-changing data distributions, including evolving patient population and new diagnosis assays, it is crucial to introduce methods for alleviating the such model forgetting. To this end, continual learning (CL) models are promising candidates. However, to our best knowledge, there\u2019s no systematic study of CL models in DP-specific applications. Here, we propose various CL scenarios in DP settings, where histopathology image data from different sources/distributions arrive sequentially, the knowledge of which is integrated into a single model without training all the data from scratch. To benchmark the performance of recently proposed continual learning algorithms in the proposed CL scenarios, We augmented a dataset for colorectal cancer H&E classification to simulate shifts of image appearance and evaluated CL methods on this dataset. Furthermore, we leveraged a breast cancer H&E dataset along with the colorectal cancer dataset to assess continual learning methods for learning from multiple tumor types. We revealed promising results of CL in DP applications, potentially paving the way for application of these methods in clinical practice.",
        "authors": "Veena Kaustaban, Qinle Ba, Ipshita Bhattacharya, Nahil Sobh, Satarupa Mukherjee, Jim Martin, Mohammad Saleh Miri, Christoph Guetter, Amal Chaturvedi",
        "award": "",
        "id": "93",
        "melba": "False",
        "or_id": "e6B-OAcJfuD",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S093.pdf",
        "title": "Characterizing Continual Learning Scenarios for Tumor Classification in Histopathology Images",
        "yt_full": "https://youtu.be/gKZtkHgN2vQ"
    },
    "S094": {
        "abstract": "Detection of anomalies before they are included in the downstream diagnosis/prognosis models is an important criterion for maintaining the medical AI imaging model performance across internal and external datasets. However, the core challenges are: (i) given the infinite variations of possible anomaly, curation of training data is in-feasible; (ii) making assumptions about the types of anomalies are often hypothetical. We propose an unsupervised anomaly detection model using a cascade variational autoencoder coupled with a zero-shot learning (ZSL) network that maps the latent vectors to semantic attribute space. We present the performance of the proposed model on two different use cases \u2013 skin images and chest radiographs and also compare against the same class of state-of-the art generative OOD detection models. ",
        "authors": "Gokul Ramasamy, Bhavik N. Patel, Imon Banerjee",
        "award": "",
        "id": "94",
        "melba": "False",
        "or_id": "VmFdXXpVx8",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T51",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Anomaly Detection using Cascade Variational Autoencoder Coupled with Zero Shot Learning ",
        "yt_full": ""
    },
    "S097": {
        "abstract": "Recent work demonstrates that images from various chest X-ray datasets contain visual features that are strongly correlated with protected demographic attributes like race and gender. This finding raises issues of fairness, since some of these factors may be used by downstream algorithms for clinical predictions. In this work, we propose a framework, using generative adversarial networks (GANs), to visualize what features are most different between X-rays belonging to two demographic subgroups.",
        "authors": "Hao Liang, Kangqi Ni, Guha Balakrishnan",
        "award": "",
        "id": "97",
        "melba": "False",
        "or_id": "bFb3V8ALx4W",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W38",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Visualizing chest X-ray dataset biases using GANs",
        "yt_full": ""
    },
    "S098": {
        "abstract": "Data augmentation (DA), an effective regularization technique, generates training samples to enhance the diversity of data and the richness of label information for training modern deep learning models. mixup, a popular recent DA method, augments training datasets with convex combinations of original samples pairs, but can generate undesirable samples, with data being sampled off the manifold and with incorrect labels. In this work, we propose \u03b6-mixup, a generalization of mixup with provably and demonstrably desirable properties that allows for convex combinations of $N \\geq 2$ samples, thus leading to more realistic and diverse outputs that incorporate information from $N$ original samples using a $p$-series interpolant. We show that, compared to mixup, \u03b6-mixup better preserves the intrinsic dimensionality of the original datasets, a desirable property for training generalizable models, and is at least as fast as mixup. Evaluation on several natural and medical image datasets shows that \u03b6-mixup outperforms mixup, CutMix, and traditional DA methods.",
        "authors": "Kumar Abhishek, Colin Joseph Brown, Ghassan Hamarneh",
        "award": "",
        "id": "98",
        "melba": "False",
        "or_id": "iXjsAarmqn",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S098.pdf",
        "title": "\u03b6-mixup: Richer, More Realistic Mixing of Multiple Images",
        "yt_full": "https://youtu.be/K1Y8kMdaqjo"
    },
    "S099": {
        "abstract": "B-cell lymphomas are complex entities consisting of a component of malignant B-cells admixed in a local tumor microenvironment (TME) inflammatory milieu.  Discrete characterization of both compartments can drive deeper understanding of pathophysiology, allowing more accurate diagnoses and prognostic predictions. However, limitations in both pathologist time and input tissue to generate multiple stains can greatly limit accurate identification of minute, cellular-level regions of interest necessary to achieve the full potential of spatial biology.  Here, we present a novel method to perform precise sampling of cells for transcriptomic analysis using virtual staining of autofluorescence images via deep learning algorithms. We validated the performance of the model on regions of interest (ROIs) identified on chemically stained images by board certified pathologists against virtually stained images. The results confirmed the usability and accuracy of the workflow and identified distinct transcriptomic profiles across a range of virtually identified ROIs, raising the possibility of our workflow\u2019s applicability across a broader range of pathologies and tissue types.",
        "authors": "Zihang Fang, Raymond Kozikowski, Kevin de Haan, Serge Alexanian, Michael E. Kallen, Alyssa Rosenbloom, Charlie Glaser, Mark Conner, Yan Liang, Kyla Teplitz, Joachim Schmid, Jaemyeong Jung, Yair Rivenson",
        "award": "",
        "id": "99",
        "melba": "False",
        "or_id": "PuLDnZVTH_f",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T52",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Virtual staining overlay enabled combined morphological and spatial transcriptomic analysis of individual malignant B cells and local tumor microenvironments",
        "yt_full": ""
    },
    "S100": {
        "abstract": "In this work we investigated the potential sex differences in white matter aging using conditional variational autoencoder (cVAE) on myelin content MR images. The cVAE model was trained along with a supervised brain age prediction model, which learns the representation of myelination aging process within a single end-to-end model architecture. The training was conducted on a normal aging dataset (CamCAN) that included 708 individual MR images. Our brief exploration revealed that women might have slightly less white matter myelination than men do at an older age. Additionally, our brain age prediction model suggested different aging regressions for men and women.",
        "authors": "Jinghang Li, Linghang Wang, Chang-le Chen, Tamer Ibrahim, Howard Aizenstein, Minjie Wu",
        "award": "",
        "id": "100",
        "melba": "False",
        "or_id": "dPWotG03R-h",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W39",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Investigate Sex Dimorphism of Cerebral Myelination Across Lifespan by Leveraging Conditional Variational Autoencoder",
        "yt_full": ""
    },
    "S101": {
        "abstract": "Hypomimia is a prevalent symptom of Parkinson's Disease(PD). It is characterized by reduced facial expression and delayed facial movement. The work proposes a framework to use Graph Neural Network(GNN) to extract related action unit(AU) features on the facial smiling videos to help to improve the recognition of hypomimia with PD. AU is an effective representation of the facial state and movement, while GNN has great capability to present relationship information between facial areas. A related AU representation can pay more attention to the relationships between the facial areas in order to increase the accuracy of the diagnosis. Experiments were conducted using an in-house dataset of 105 facial smiling videos, which contains 55 healthy control(HC) participants and 50 PD patients. Our method's performance was compared to that of random forest (RF) and support vector machine (SVM) classifiers.  Our method achieved an Accuracy, PPV, TPR, and F1 score of {91.7%, 92.8%, 90.6%, 91.7%}, while the RF and SVM achieved {84.5%,84.8%, 82.7\\%, 83.7%} and {88.7%, 88.0%, 88,7%, 88.3%} respectively on the dataset. ",
        "authors": "Yingjing Xu, Bo Lin, Wei Luo, Shuiguang Deng, Jianwei Yin",
        "award": "",
        "id": "101",
        "melba": "False",
        "or_id": "BLWmZy6kSL7",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S101.pdf",
        "title": "Facial AU-aid hypomimia diagnosis based on GNN",
        "yt_full": ""
    },
    "S102": {
        "abstract": "Recent development in magnetic resonance (MR) harmonization has facilitated the synthesis of varying MR image contrasts while preserving the underlying anatomical structures. This enables an investigation into the impact of different T1-weighted (T1-w) MR image contrasts on the performance of deep learning-based algorithms, allowing the identification of optimal MR image contrasts for pretrained algorithms. In this study, we employ image harmonization to examine the influence of diverse T1-w MR image contrasts on the state-of-the-art ventricle parcellation algorithm, VParNet. Our results reveal the existence of an optimal operating contrast~(OOC) for VParNet ventricle parcellation, achieved by synthesizing T1-w MR images with a range of contrasts. The OOC for VParNet is not of the same MR image contrast of any of the training data. Experiments conducted on healthy subjects and post-surgical NPH patients demonstrate that adjusting the MR image contrast to the OOC significantly enhances the performance of a pretrained VParNet, thereby improving its clinical applicability.",
        "authors": "Savannah Hays, Lianrui Zuo, Yuli Wang, Mark G Luciano, Aaron Carass, Jerry L Prince",
        "award": "",
        "id": "102",
        "melba": "False",
        "or_id": "3ndjE9eawkr",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T53",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Exploring the Optimal Operating MR Contrast for Brain Ventricle Parcellation",
        "yt_full": ""
    },
    "S104": {
        "abstract": "Medical image classification is one of the most important tasks for computer-aided diagnosis. Deep learning models, particularly convolutional neural networks have been successfully used for disease classification from medical images, facilitated with automated feature learning. However, the diverse imaging modalities and clinical pathologies makes it challenging to generalized and robust classification. Towards improving the model performance, we propose a novel pretraining approach, namely \\textbf{Forward Forward Contrastive Learning (FFCL)} which leverages the Forward-Forward Algorithm in a contrastive learning framework--both locally and globally. Our experimental results on chest X-ray dataset indicate that the proposed FFCL achieves superior performance (\\textbf{3.69\\%} accuracy over ImageNet pretrained ResNet-18) over existing pretraining models in pneumonia classification task. Moreover, an extensive ablation experiments support the particular local and global contrastive pretraining design in FFCL.",
        "authors": "Md. Atik Ahamed, Jin Chen, Abdullah Al Zubaer Imran",
        "award": "",
        "id": "104",
        "melba": "False",
        "or_id": "tfEylAl8vf",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W47",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "FFCL: Forward-Forward Contrastive Learning for Improved Medical Image Classification",
        "yt_full": ""
    },
    "S108": {
        "abstract": "Automated chest X-ray report generation using machine learning has emerged as a promising technology for improving the accuracy and efficiency of chest X-ray interpretation. In this paper, we present a novel approach for automated report generation that combines the power of vision transformers for image information encoding and PubMedBERT for text decoding. Our model extracts image features using a vision transformer and text features using PubMedBERT. The encoded features are then fed into a text decoder to generate standardized reports. We trained our model on a  dataset of chest X-rays and corresponding report findings (IU dataset)  and evaluated its performance on a small subset of the MIMIC-CXR dataset. ",
        "authors": "Yuhang Jiang, Shashank Gupta, Abdullah Al Zubaer Imran",
        "award": "",
        "id": "108",
        "melba": "False",
        "or_id": "YEMH26an2bM",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M49",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Transforming Radiology Workflows: Pretraining for Automated Chest X-ray Report Generation",
        "yt_full": ""
    },
    "S109": {
        "abstract": "Functional analysis of the left atrium (LA) plays an increasingly important role in the prognosis and diagnosis of cardiovascular diseases. Echocardiography-based measurements of LA dimensions and strains are useful biomarkers, but they provide an incomplete picture of atrial deformations. High-resolution dynamic magnetic resonance images (Cine MRI) offer the opportunity to examine LA motion and deformation in 3D, at higher spatial resolution and with full LA coverage. However, there are no dedicated tools to automatically characterise LA motion in 3D. Thus, we propose a tool that automatically segments the LA and extracts the displacement fields across the cardiac cycle. The pipeline is able to accurately track the LA wall across the cardiac cycle with an average Hausdorff distance of $2.51 \\pm 1.3~mm$ and Dice score of $0.96 \\pm 0.02$.",
        "authors": "Christoforos Galazis, Anil Anthony Bharath, Marta Varela",
        "award": "",
        "id": "109",
        "melba": "False",
        "or_id": "_bAp02OXNiT",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T54",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "High-resolution 3D Maps of Left Atrial Displacements using an Unsupervised Image Registration Neural Network",
        "yt_full": ""
    },
    "S110": {
        "abstract": "Cine steady-state free-precession (SSFP) is the backbone of cardiac MRI, providing visualization of cardiac structure and function over the cardiac cycle, but requires concurrent ECG-gating to combine k-space data over multiple heart beats. However, cine SSFP is limited by a number of factors including arrhythmia, where beat-to-beat variability causes image artifacts. Real-time (RT) SSFP and recent innovations in image reconstruction provides a new potential alternative, capable of acquiring images without averaging over multiple heart beats. However, analysis of cardiac function from this image data can be complex, requiring retrospective analysis of function over multiple cardiac cycles and slices.  We propose a deep learning regression method to facilitate cardiac phase detection, leveraging synthetic training approach from historical cine SSFP image data, and evaluate the effectiveness of this approach for detecting cardiac phase on RT SSFP images, manually labeled by expert readers. This combined approach using RT SSFP may have multiple potential advantages over traditional cine SSFP for evaluating cardiac function in patients with arrhythmia or difficulty tolerating long breath holds.",
        "authors": "Samira Masoudi, Amin Mahmoodi, Hafsa Babar, Albert Hsiao",
        "award": "",
        "id": "110",
        "melba": "False",
        "or_id": "5063TZgHfQm",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W41",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\nWednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "/virtual/poster/S110.pdf",
        "title": "Deep Learning Regression of Cardiac Phase on Real-Time MRI",
        "yt_full": "https://youtu.be/_V7RsdtVSpo"
    },
    "S112": {
        "abstract": "Accurately determining contrast phase in an abdominal computed tomography (CT) series is an important step prior to deploying downstream artificial intelligence methods trained to operateon the specific series. Inspired by how radiologists assess contrast phase status, this paper presents a simple approach to automatically detect the contrast phase. This method combines features extracted from the segmentation of key anatomical structures with a gradient boosting classifier for this task. The algorithm demonstrates high accuracy in categorizing the images into non-contrast (96.6\\% F1 score), arterial (78.9\\% F1 score), venous (92.2\\% F1 score), and delayed phases (95.0\\% F1 score), making it a valuable tool for enhancing AI applicability in medical imaging.",
        "authors": "Eduardo Pontes Reis, Louis Blankemeier, Juan Manuel Zambrano Chaves, Malte Jensen, Sally Yao, Cesar Augusto Madid Truyts, Marc H. Willis, Robert D. Boutin, Edson Amaro Jr, Akshay S Chaudhari",
        "award": "",
        "id": "112",
        "melba": "False",
        "or_id": "B8e-iS9j43",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S112.pdf",
        "title": "Automatic Contrast Phase Detection on Abdominal Computed Tomography using Clinically-Inspired Techniques",
        "yt_full": "https://youtu.be/LFzh_w15hSM"
    },
    "S113": {
        "abstract": "In limited data settings, transfer learning has proven useful in initializing model parameters. In this work, we compare random initialization, pre-training on ImageNet, and pre-training on histopathology datasets for 2 model architectures across 4 segmentation histopathology datasets. We show that pre-training on histopathology datasets does not always significantly improve performance relative to ImageNet pre-trained weights for both model architectures. We conclude that unless larger labeled datasets or semi-supervised techniques are leveraged, ImageNet pre-trained weights should be used in initializing segmentation models for histopathology.  ",
        "authors": "Payden McBee, Nazanin Moradinasab, Donald E Brown, Sana Syed",
        "award": "",
        "id": "113",
        "melba": "False",
        "or_id": "LkXKbvcK_c",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "T55",
        "schedule": "Tuesday, July 11: Posters \u2014 10:30\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Pre-training Segmentation Models for Histopathology",
        "yt_full": ""
    },
    "S114": {
        "abstract": "The development of fluorescence light sheets and expansion microscopy (ExM) in recent years enables the visualization of detailed neural structures to help unlock the secrets of neural functioning. Deep learning techniques have then become essential tools to process the ever-increasing amount of high-quality and high-resolution images. In this study, we developed a single-scale deconvolution model for extracting multiscale deconvoluted response (MDR) from the volumes of microscopy images of neurons and generative models to translate images between the lateral and axial views. The results demonstrated that deep learning as a promising tool in approving image volume quality and comprehension of structural information of light sheet microscopy.",
        "authors": "Meng-Yun Wu, Da-Yu Huang, Ya-Ding Liu, Li-An Chu, Gary.  Han Chang",
        "award": "",
        "id": "114",
        "melba": "False",
        "or_id": "NiUSj5tDKf",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S114.pdf",
        "title": "Expansion Microscopy Imaging Isotropic Restoration by Unsupervised Deep Learning",
        "yt_full": "https://youtu.be/YFv548AxPy8"
    },
    "S116": {
        "abstract": "We propose a novel diagnostic method for clonal hematopoiesis of indeterminate potential (CHIP), a condition characterized by the presence of somatic mutations in hematopoietic stem cells without detectable hematologic malignancy, using deep-learning techniques. We developed a convolutional neural network (CNN) to predict CHIP status using 4 different views from standard delayed gadolinium-enhanced cardiac MRI. We used 5-fold cross validation on 82 patients to assess the performance of our model. Different algorithms were compared to find the optimal patient-level prediction method using the image-level CNN predictions. We found that the best model had an AUC of 0.85 and an accuracy of 82%. We conclude that a deep learning-based diagnostic approach for CHIP is promising.",
        "authors": "Sangeon Ryu, Shawn Ahn, Jeacy Espinoza, Alokkumar Jha, Stephanie Halene, James s Duncan, Jennifer Kwan, Nicha C Dvornek",
        "award": "",
        "id": "116",
        "melba": "False",
        "or_id": "B97_xzj69FK",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W12",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "A Novel Approach for Assessment of Clonal Hematopoiesis of Indeterminate Potential Using Deep Neural Networks",
        "yt_full": ""
    },
    "S117": {
        "abstract": "Parallel MRI techniques in the k-space, like GRAPPA are widely used in accelerated MRI. Recently neural-network based non-linear approaches have shown improved performance over linear methods like GRAPPA. But present day neural networks are largely tailored to process real data, hence the complex-valued k-space data is processed as two-dimensional real data in these. In this work, we study the performance of an end-to-end complex-valued architecture trained using complex-valued optimization, for interpolating missing values in the k-space for parallel MR which we call the Complex rRAKI. We propose a generalized version of the ReLU activation function on the complex plane called the PlaneReLU. The performance of the Complex rRAKI is evaluated on two publicly-available k-space MRI datasets, the fastmri multicoil brain dataset and the fastmri multicoil knee dataset. Com- parison of obtained results with those on the baseline rRAKI are also presented. The proposed Complex rRAKI achieves improved performance over the baseline with respect to standard metrics SSIM and NRMSE with 50% fewer parameters.",
        "authors": "Poornima Jain, Neelam Sinha, G. Srinivasaraghavan",
        "award": "",
        "id": "117",
        "melba": "False",
        "or_id": "7mwxN2h7SM",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S117.pdf",
        "title": "An end-to-end Complex-valued Neural Network approach for k-space interpolation in Parallel MRI",
        "yt_full": "https://youtu.be/2PaXyy2Bwt4"
    },
    "S118": {
        "abstract": "Spatial tissue imaging technologies enable highly resolved spatial characterization of cellular phenotypes. Today this spatial mapping still largely depends on laborious manual annotation and molecular labels to understand tissue organization. As a result, we are not optimally leveraging higher-order patterns of cell organization potentially connected to disease pathology or clinical outcomes. To address this gap, we propose a novel approach how autoencoders with spatial attention mechanism can be trained to enrich cell phenotyping. Our approach combines information on cellular phenotypes with the physical proximity of cells to accurately identify organ-specific microanatomical in the tumor microenvironment. We apply our method to lung tumor tissues imaging mass cytometry data to show how it can detect higher-level cell organizations and information on structural differences.",
        "authors": "Diane Vincent, Alice Gosselin, Nasire Mahmudi, Yassine janati, Elton Rexhepaj",
        "award": "",
        "id": "118",
        "melba": "False",
        "or_id": "PUs7MSra82U",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W42",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Applying spatial attention-based autoencoder learning of latent representation for unsupervised characterization of tumor microenvironment",
        "yt_full": ""
    },
    "S119": {
        "abstract": "The Levator Ani Muscle (LAM) deficit is a common side effect of vaginal childbirth and is linked to pelvic organ prolapse (POP) and other pelvic floor complications. Diagnosis and treatment of these complications require imaging and examining the pelvic floor, which is a time-consuming process subject to operator variability. We proposed using deep learning (DL) methods to automatically segment LAM from 3D endovaginal ultrasound images (EVUS) to improve diagnostic accuracy and efficiency. Over one thousand images extracted from 3D EVUS data consisting of healthy subjects and patients with pelvic floor disorders were utilized for the automated LAM segmentation. U-Net, FD-U-Net, Attention U-Net were implemented with Dice and Intersection over Union (IoU) used for model performance evaluation. The U-Net based models had 0.84-0.86 mean Dice score, which demonstrated efficacy compared to literature in LAM segmentation. Our study demonstrated the feasibility of DL-segmentation using U-Net and its variants for automated LAM segmentation from 3D EVUS images and has potential of being implemented in AI-based diagnostic tools for improved management of pelvic floor disorders, especially in low socioeconomic regions. ",
        "authors": "Amad Qureshi, Nada Rabbat, Ko-Tsung Hsu, Zara Asif, Parag Chitnis, Abbas Shobeiri, Qi Wei",
        "award": "",
        "id": "119",
        "melba": "False",
        "or_id": "hYnou0zo0PC",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M51",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Deep Learning based Automatic Segmentation of the Levator Ani Muscle from 3D Endovaginal Ultrasound Images",
        "yt_full": ""
    },
    "S120": {
        "abstract": "Recent advancements in zero-shot learning have enabled the use of paired image-text data to replace structured labels, replacing the need for expert annotated datasets. Domain pre-trained models, such as CXR-BERT, BlueBERT, and ClinicalBERT, offer the potential to improve the performance of CLIP-like models with specific domain knowledge by replacing BERT weights at the cost of breaking the original model's alignment. We evaluate the performance of zero-shot classification models with domain-specific pre-training for detecting low-prevalence pathologies. Even though replacing the weights of the original CLIP-BERT degrades model performance on commonly found pathologies, we show that pre-trained text towers perform exceptionally better on low-prevalence diseases. This motivates future ensemble models with a combination of differently trained language models for maximal performance.",
        "authors": "Aakash Mishra, Rajat Mittal, Christy Jestin, Kostas Tingos, Pranav Rajpurkar",
        "award": "",
        "id": "120",
        "melba": "False",
        "or_id": "tQvYo-DMrO",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S120.pdf",
        "title": "Improving Zero-Shot Detection of Low Prevalence Chest Pathologies using Domain Pre-trained Language Models",
        "yt_full": "https://youtu.be/mFRnZMp_X_k"
    },
    "S121": {
        "abstract": "Non-uniform bias field due to external factors hampers quantitative MR image analysis. For reliable quantitative MR image analysis, appropriate correction for the bias field is necessary. In this study, we propose Hampel denoising diffusion model to effectively correct the bias field from MR images. Compared with N4 and Gaussian denoising diffusion models, the proposed model provided higher PSNRs, SSIMs and lower MSEs. Higher efficiency could be achieved compared to N4 when our model takes 9 times faster in inference time.",
        "authors": "Junhyeok Lee, Junghwa Kang, Yoonho Nam, TaeYoung Lee",
        "award": "",
        "id": "121",
        "melba": "False",
        "or_id": "Ob7xQXamjo_",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S121.pdf",
        "title": "Bias Field Correction in MRI with Hampel Noise Denoising Diffusion Probabilistic Model",
        "yt_full": "https://youtu.be/EEXFKUhQdO4"
    },
    "S122": {
        "abstract": "Detecting the treatment response of radiotherapy for soft-tissue sarcomas (STS) is difficult due to the intratumoral heterogeneity of the disease within tumours. Radiomics and deep learning offer opportunities to find novel biomarkers of treatment response. Small sample sizes are also a common challenge when using modeling problems due to it being rare cancer. We investigate the use of representation learning by developing an unsupervised deep learning pipeline that can learn disentangled and interpretable features from the data. We look at the pairwise correlation between the features and also evaluated their baseline test-retest repeatability because having good baseline repeatability is important for these features to be useful for personalized treatment. We demonstrate that the features generated by this approach using are both pairwise independent and stable upon repeat baseline measurement. We also investigated the use of these features alongside clinical features in predicting recurrence-free survival in a pilot cohort. ",
        "authors": "Timothy Sum Hon Mun, Amani Arthur, Imogen Thrussell, Jessica Winfield, Dow-Mu Koh, Paul Huang, Christina Messiou, Simon Doran, Matthew Blackledge",
        "award": "",
        "id": "122",
        "melba": "False",
        "or_id": "BNL83dNAiE",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M52",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Radiomics using disentangled latent features from deep representation learning in soft-tissue sarcoma",
        "yt_full": ""
    },
    "S123": {
        "abstract": "Anterior segment optical coherence tomography (AS-OCT) is a recent imaging technique for visualizing the physiological structure of the anterior segment. The speckle noise inherited in ASOCT images degrades the visual quality and hampers the subsequent medical analysis. Previous work was devoted to removing the speckles and acquiring satisfying images. According to the clinical requirements, it might be desirable to maintain locally higher data fidelity instead of enforcing visually appealing but rather wrong image structural features. Catering to this expectation, we propose a Consistent Structural Generative Adversarial Network (CSGAN) to learn the clean style of low-speckle in repeated AS-OCT images and simultaneously preserve the tiny but vital structural knowledge among the latent feature, spatial and frequency domains. Specifically, we design a latent constraint into the generator to capture the inherent content in the feature domain and adopt the perceptual similarities to directly preserve structural detail in the spatial dimension. Besides, we introduce a focal frequency scheme that adaptively represents and distinguishes hard frequencies to compensate for the spatial loss and refine the generated image to improve image quality. Finally, the experimental results demonstrate that the CSGAN can achieve satisfactory despeckling results with preserving structural details on the AS-Casia dataset.",
        "authors": "Sanqian Li, Muxing Xiong, Risa Higashita, Jiang Liu",
        "award": "",
        "id": "123",
        "melba": "False",
        "or_id": "JY4oJg6-gc",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "Virtual only",
        "schedule": "Wednesday, July 12: Virtual poster session - 8:00\u20139:00\n",
        "short": "True",
        "slides": "/virtual/poster/S123.pdf",
        "title": "CSGAN: a consistent structural GAN for AS-OCT image despeckling by image translation",
        "yt_full": "https://youtu.be/gtY_1ATeWAo"
    },
    "S126": {
        "abstract": "We hypothesized that state-of-the-art AI models, trained using thousands of patient cases, are non-inferior to radiologists at clinically significant prostate cancer diagnosis using MRI. To test the same, we designed an international comparative study titled the PI-CAI challenge, where we investigated AI models that were independently developed, trained and externally tested using a large multi-center cohort of 10,207 patient exams. Preliminary results indicate that when trained on 1,500 cases only, such models already achieve diagnostic performance comparable to that of radiologists reported in literature.",
        "authors": "Anindo Saha, Joeran Bosma, Jasper Twilt, Bram van Ginneken, Derya Yakar, Mattijs Elschot, Jeroen Veltman, Jurgen F\u00fctterer, Maarten de Rooij, henkjan huisman",
        "award": "",
        "id": "126",
        "melba": "False",
        "or_id": "XfXcA9-0XxR",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W43",
        "schedule": "Monday, July 10: MIDL board special session \u2014 10:30\u201311:00\nWednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI - The PI-CAI Challenge",
        "yt_full": ""
    },
    "S127": {
        "abstract": "Accurate segmentation of perivascular space (PVS) is essential for its quantitative analysis and clinical applications. Various segmentation methods have been proposed, but semi-supervised learning methods have never been attempted. Here, a 3D multi-channel, multi-scale semi-supervised PVS segmentation (M2SS-PVS) network is proposed. The proposed network incorporated multi-scale image features in the encoder and applied a few strategies to mitigate class imbalance issue. The proposed M2SS-PVS network segmented PVS with the highest accuracy and high sensitivity among all the tested supervised and semi-supervised methods.",
        "authors": "Yaqiong Chai, Hedong Zhang, Gilsoon Park, Erika Lopez, Cong Zang, Jongmok Ha, Hosung Kim",
        "award": "",
        "id": "127",
        "melba": "False",
        "or_id": "e0QulGVGCS",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M53",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Semi-supervised learning in perivascular space segmentation using MRI images",
        "yt_full": ""
    },
    "S129": {
        "abstract": "Automated dental diagnosis requires accurate segmentation of tooth from cone-beam computed tomography (CBCT) images. However, existing segmentation methods often neglect incorporating prior information and symptoms of tooth, which can cause unsatisfactory segmentation performance on tooth with symptoms. To this respect, we propose Tooth Symptom Network (TSNet), consisting of Dental Prior Guiding Data Augmentation (DPGDA) and Dental Symptom Shape Loss (DSSL), to improve segmentation performance for tooth with different clinical symptoms. Experiments show that TSNet outperforms all state-of-the-art methods across datasets with all kinds of symptoms with an average increase of 1.13\\% in Dice and 2.00\\% in IoU.",
        "authors": "Linjie Tong, Jiaxiang Liu, YANG FENG, Tianxiang Hu, Zuozhu Liu",
        "award": "",
        "id": "129",
        "melba": "False",
        "or_id": "FCYGwhzF7E",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "W44",
        "schedule": "Wednesday, July 12: Posters \u2014 10:15\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "TSNet: Integrating Dental Position Prior and Symptoms for Tooth Segmentation from CBCT Images",
        "yt_full": ""
    },
    "S130": {
        "abstract": "Panoramic radiographs, also known as orthopantomograms, are routinely used by dentists to get a better understanding of the patient's oral condition and perform orthodontics; however, most dentists' use of them is rough which may cause medical negligence due to physician burnout and time limitation. To reduce the dentists' workflow, we define a mission to segment five orals structures including Alveolarcrest, Condyle, Neuraltube, Sinusmaxillaris, and Teeth on Panoramic radiographs. A Cascaded Multi-scale Mask2former(CMMask2former) method is proposed for this task. For the small objects, we specially design multi-scale masked attention for the mask area. Overall, the whole structure is designed in a cascade way of two stages for localization and prediction. Our results demonstrate a superior prediction performance compared to other methods.",
        "authors": "Kaiwei Sun, YANG FENG, Zuozhu Liu",
        "award": "",
        "id": "130",
        "melba": "False",
        "or_id": "7_jig8Y3pt",
        "oral": "False",
        "pmlr_url": "",
        "poster_loc": "M54",
        "schedule": "Monday, July 10: Posters \u2014 11:00\u201312:00 & 15:00\u201316:00\n",
        "short": "True",
        "slides": "",
        "title": "Benchmark and Boosted Segmentation on Dental Panoramic Radiographs",
        "yt_full": ""
    }
}