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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.07299">arXiv:2410.07299</a> <span> [<a href="https://arxiv.org/pdf/2410.07299">pdf</a>, <a href="https://arxiv.org/format/2410.07299">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Towards Generalisable Time Series Understanding Across Domains </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Turgut%2C+%C3%96">脰zg眉n Turgut</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCller%2C+P">Philip M眉ller</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.07299v1-abstract-short" style="display: inline;"> In natural language processing and computer vision, self-supervised pre-training on large datasets unlocks foundational model capabilities across domains and tasks. However, this potential has not yet been realised in time series analysis, where existing methods disregard the heterogeneous nature of time series characteristics. Time series are prevalent in many domains, including medicine, enginee… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.07299v1-abstract-full').style.display = 'inline'; document.getElementById('2410.07299v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.07299v1-abstract-full" style="display: none;"> In natural language processing and computer vision, self-supervised pre-training on large datasets unlocks foundational model capabilities across domains and tasks. However, this potential has not yet been realised in time series analysis, where existing methods disregard the heterogeneous nature of time series characteristics. Time series are prevalent in many domains, including medicine, engineering, natural sciences, and finance, but their characteristics vary significantly in terms of variate count, inter-variate relationships, temporal dynamics, and sampling frequency. This inherent heterogeneity across domains prevents effective pre-training on large time series corpora. To address this issue, we introduce OTiS, an open model for general time series analysis, that has been specifically designed to handle multi-domain heterogeneity. We propose a novel pre-training paradigm including a tokeniser with learnable domain-specific signatures, a dual masking strategy to capture temporal causality, and a normalised cross-correlation loss to model long-range dependencies. Our model is pre-trained on a large corpus of 640,187 samples and 11 billion time points spanning 8 distinct domains, enabling it to analyse time series from any (unseen) domain. In comprehensive experiments across 15 diverse applications - including classification, regression, and forecasting - OTiS showcases its ability to accurately capture domain-specific data characteristics and demonstrates its competitiveness against state-of-the-art baselines. Our code and pre-trained weights are publicly available at https://github.com/oetu/otis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.07299v1-abstract-full').style.display = 'none'; document.getElementById('2410.07299v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.08410">arXiv:2407.08410</a> <span> [<a href="https://arxiv.org/pdf/2407.08410">pdf</a>, <a href="https://arxiv.org/format/2407.08410">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Specialist vision-language models for clinical ophthalmology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Holland%2C+R">Robbie Holland</a>, <a href="/search/cs?searchtype=author&query=Taylor%2C+T+R+P">Thomas R. P. Taylor</a>, <a href="/search/cs?searchtype=author&query=Holmes%2C+C">Christopher Holmes</a>, <a href="/search/cs?searchtype=author&query=Riedl%2C+S">Sophie Riedl</a>, <a href="/search/cs?searchtype=author&query=Mai%2C+J">Julia Mai</a>, <a href="/search/cs?searchtype=author&query=Patsiamanidi%2C+M">Maria Patsiamanidi</a>, <a href="/search/cs?searchtype=author&query=Mitsopoulou%2C+D">Dimitra Mitsopoulou</a>, <a href="/search/cs?searchtype=author&query=Hager%2C+P">Paul Hager</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCller%2C+P">Philip M眉ller</a>, <a href="/search/cs?searchtype=author&query=Scholl%2C+H+P+N">Hendrik P. N. Scholl</a>, <a href="/search/cs?searchtype=author&query=Bogunovi%C4%87%2C+H">Hrvoje Bogunovi膰</a>, <a href="/search/cs?searchtype=author&query=Schmidt-Erfurth%2C+U">Ursula Schmidt-Erfurth</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Sivaprasad%2C+S">Sobha Sivaprasad</a>, <a href="/search/cs?searchtype=author&query=Lotery%2C+A+J">Andrew J. Lotery</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.08410v1-abstract-short" style="display: inline;"> Clinicians spend a significant amount of time reviewing medical images and transcribing their findings regarding patient diagnosis, referral and treatment in text form. Vision-language models (VLMs), which automatically interpret images and summarize their findings as text, have enormous potential to alleviate clinical workloads and increase patient access to high-quality medical care. While found… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.08410v1-abstract-full').style.display = 'inline'; document.getElementById('2407.08410v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.08410v1-abstract-full" style="display: none;"> Clinicians spend a significant amount of time reviewing medical images and transcribing their findings regarding patient diagnosis, referral and treatment in text form. Vision-language models (VLMs), which automatically interpret images and summarize their findings as text, have enormous potential to alleviate clinical workloads and increase patient access to high-quality medical care. While foundational models have stirred considerable interest in the medical community, it is unclear whether their general capabilities translate to real-world clinical utility. In this work, we show that foundation VLMs markedly underperform compared to practicing ophthalmologists on specialist tasks crucial to the care of patients with age-related macular degeneration (AMD). To address this, we initially identified the essential capabilities required for image-based clinical decision-making, and then developed a curriculum to selectively train VLMs in these skills. The resulting model, RetinaVLM, can be instructed to write reports that significantly outperform those written by leading foundation medical VLMs in disease staging (F1 score of 0.63 vs. 0.11) and patient referral (0.67 vs. 0.39), and approaches the diagnostic performance of junior ophthalmologists (who achieve 0.77 and 0.78 on the respective tasks). Furthermore, in a reader study involving two senior ophthalmologists with up to 32 years of experience, RetinaVLM's reports were found to be similarly correct (78.6% vs. 82.1%) and complete (both 78.6%) as reports written by junior ophthalmologists with up to 10 years of experience. These results demonstrate that our curriculum-based approach provides a blueprint for specializing generalist foundation medical VLMs to handle real-world clinical tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.08410v1-abstract-full').style.display = 'none'; document.getElementById('2407.08410v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to Nature Medicine</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.09549">arXiv:2405.09549</a> <span> [<a href="https://arxiv.org/pdf/2405.09549">pdf</a>, <a href="https://arxiv.org/format/2405.09549">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Holland%2C+R">Robbie Holland</a>, <a href="/search/cs?searchtype=author&query=Kaye%2C+R">Rebecca Kaye</a>, <a href="/search/cs?searchtype=author&query=Hagag%2C+A+M">Ahmed M. Hagag</a>, <a href="/search/cs?searchtype=author&query=Leingang%2C+O">Oliver Leingang</a>, <a href="/search/cs?searchtype=author&query=Taylor%2C+T+R+P">Thomas R. P. Taylor</a>, <a href="/search/cs?searchtype=author&query=Bogunovi%C4%87%2C+H">Hrvoje Bogunovi膰</a>, <a href="/search/cs?searchtype=author&query=Schmidt-Erfurth%2C+U">Ursula Schmidt-Erfurth</a>, <a href="/search/cs?searchtype=author&query=Scholl%2C+H+P+N">Hendrik P. N. Scholl</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Lotery%2C+A+J">Andrew J. Lotery</a>, <a href="/search/cs?searchtype=author&query=Sivaprasad%2C+S">Sobha Sivaprasad</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.09549v1-abstract-short" style="display: inline;"> Diseases are currently managed by grading systems, where patients are stratified by grading systems into stages that indicate patient risk and guide clinical management. However, these broad categories typically lack prognostic value, and proposals for new biomarkers are currently limited to anecdotal observations. In this work, we introduce a deep-learning-based biomarker proposal system for the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.09549v1-abstract-full').style.display = 'inline'; document.getElementById('2405.09549v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.09549v1-abstract-full" style="display: none;"> Diseases are currently managed by grading systems, where patients are stratified by grading systems into stages that indicate patient risk and guide clinical management. However, these broad categories typically lack prognostic value, and proposals for new biomarkers are currently limited to anecdotal observations. In this work, we introduce a deep-learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46,496 retinal optical coherence tomography (OCT) images. To interpret the discovered biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We then conduct two parallel 1.5-hour semi-structured interviews with two independent teams of retinal specialists that describe each cluster in clinical language. Overall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognised as known biomarkers already used in established grading systems and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid and thick from thin choroids, and in simulation outperformed clinically-used grading systems in prognostic value. Overall, contrastive learning enabled the automatic proposal of AMD biomarkers that go beyond the set used by clinically established grading systems. Ultimately, we envision that equipping clinicians with discovery-oriented deep-learning tools can accelerate discovery of novel prognostic biomarkers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.09549v1-abstract-full').style.display = 'none'; document.getElementById('2405.09549v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.16776">arXiv:2403.16776</a> <span> [<a href="https://arxiv.org/pdf/2403.16776">pdf</a>, <a href="https://arxiv.org/format/2403.16776">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlases </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Starck%2C+S">Sophie Starck</a>, <a href="/search/cs?searchtype=author&query=Sideri-Lampretsa%2C+V">Vasiliki Sideri-Lampretsa</a>, <a href="/search/cs?searchtype=author&query=Kainz%2C+B">Bernhard Kainz</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M">Martin Menten</a>, <a href="/search/cs?searchtype=author&query=Mueller%2C+T">Tamara Mueller</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.16776v1-abstract-short" style="display: inline;"> Anatomical atlases are widely used for population analysis. Conditional atlases target a particular sub-population defined via certain conditions (e.g. demographics or pathologies) and allow for the investigation of fine-grained anatomical differences - such as morphological changes correlated with age. Existing approaches use either registration-based methods that are unable to handle large anato… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.16776v1-abstract-full').style.display = 'inline'; document.getElementById('2403.16776v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.16776v1-abstract-full" style="display: none;"> Anatomical atlases are widely used for population analysis. Conditional atlases target a particular sub-population defined via certain conditions (e.g. demographics or pathologies) and allow for the investigation of fine-grained anatomical differences - such as morphological changes correlated with age. Existing approaches use either registration-based methods that are unable to handle large anatomical variations or generative models, which can suffer from training instabilities and hallucinations. To overcome these limitations, we use latent diffusion models to generate deformation fields, which transform a general population atlas into one representing a specific sub-population. By generating a deformation field and registering the conditional atlas to a neighbourhood of images, we ensure structural plausibility and avoid hallucinations, which can occur during direct image synthesis. We compare our method to several state-of-the-art atlas generation methods in experiments using 5000 brain as well as whole-body MR images from UK Biobank. Our method generates highly realistic atlases with smooth transformations and high anatomical fidelity, outperforming the baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.16776v1-abstract-full').style.display = 'none'; document.getElementById('2403.16776v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.07513">arXiv:2403.07513</a> <span> [<a href="https://arxiv.org/pdf/2403.07513">pdf</a>, <a href="https://arxiv.org/format/2403.07513">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Spatiotemporal Representation Learning for Short and Long Medical Image Time Series </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shen%2C+C">Chengzhi Shen</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/cs?searchtype=author&query=Bogunovi%C4%87%2C+H">Hrvoje Bogunovi膰</a>, <a href="/search/cs?searchtype=author&query=Schmidt-Erfurth%2C+U">Ursula Schmidt-Erfurth</a>, <a href="/search/cs?searchtype=author&query=Scholl%2C+H">Hendrik Scholl</a>, <a href="/search/cs?searchtype=author&query=Sivaprasad%2C+S">Sobha Sivaprasad</a>, <a href="/search/cs?searchtype=author&query=Lotery%2C+A">Andrew Lotery</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Hager%2C+P">Paul Hager</a>, <a href="/search/cs?searchtype=author&query=Holland%2C+R">Robbie Holland</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.07513v2-abstract-short" style="display: inline;"> Analyzing temporal developments is crucial for the accurate prognosis of many medical conditions. Temporal changes that occur over short time scales are key to assessing the health of physiological functions, such as the cardiac cycle. Moreover, tracking longer term developments that occur over months or years in evolving processes, such as age-related macular degeneration (AMD), is essential for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07513v2-abstract-full').style.display = 'inline'; document.getElementById('2403.07513v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.07513v2-abstract-full" style="display: none;"> Analyzing temporal developments is crucial for the accurate prognosis of many medical conditions. Temporal changes that occur over short time scales are key to assessing the health of physiological functions, such as the cardiac cycle. Moreover, tracking longer term developments that occur over months or years in evolving processes, such as age-related macular degeneration (AMD), is essential for accurate prognosis. Despite the importance of both short and long term analysis to clinical decision making, they remain understudied in medical deep learning. State of the art methods for spatiotemporal representation learning, developed for short natural videos, prioritize the detection of temporal constants rather than temporal developments. Moreover, they do not account for varying time intervals between acquisitions, which are essential for contextualizing observed changes. To address these issues, we propose two approaches. First, we combine clip-level contrastive learning with a novel temporal embedding to adapt to irregular time series. Second, we propose masking and predicting latent frame representations of the temporal sequence. Our two approaches outperform all prior methods on temporally-dependent tasks including cardiac output estimation and three prognostic AMD tasks. Overall, this enables the automated analysis of temporal patterns which are typically overlooked in applications of deep learning to medicine. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07513v2-abstract-full').style.display = 'none'; document.getElementById('2403.07513v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.06601">arXiv:2403.06601</a> <span> [<a href="https://arxiv.org/pdf/2403.06601">pdf</a>, <a href="https://arxiv.org/format/2403.06601">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Berger%2C+A+H">Alexander H. Berger</a>, <a href="/search/cs?searchtype=author&query=Lux%2C+L">Laurin Lux</a>, <a href="/search/cs?searchtype=author&query=Shit%2C+S">Suprosanna Shit</a>, <a href="/search/cs?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/cs?searchtype=author&query=Kaissis%2C+G">Georgios Kaissis</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.06601v1-abstract-short" style="display: inline;"> Direct image-to-graph transformation is a challenging task that solves object detection and relationship prediction in a single model. Due to the complexity of this task, large training datasets are rare in many domains, which makes the training of large networks challenging. This data sparsity necessitates the establishment of pre-training strategies akin to the state-of-the-art in computer visio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.06601v1-abstract-full').style.display = 'inline'; document.getElementById('2403.06601v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.06601v1-abstract-full" style="display: none;"> Direct image-to-graph transformation is a challenging task that solves object detection and relationship prediction in a single model. Due to the complexity of this task, large training datasets are rare in many domains, which makes the training of large networks challenging. This data sparsity necessitates the establishment of pre-training strategies akin to the state-of-the-art in computer vision. In this work, we introduce a set of methods enabling cross-domain and cross-dimension transfer learning for image-to-graph transformers. We propose (1) a regularized edge sampling loss for sampling the optimal number of object relationships (edges) across domains, (2) a domain adaptation framework for image-to-graph transformers that aligns features from different domains, and (3) a simple projection function that allows us to pretrain 3D transformers on 2D input data. We demonstrate our method's utility in cross-domain and cross-dimension experiments, where we pretrain our models on 2D satellite images before applying them to vastly different target domains in 2D and 3D. Our method consistently outperforms a series of baselines on challenging benchmarks, such as retinal or whole-brain vessel graph extraction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.06601v1-abstract-full').style.display = 'none'; document.getElementById('2403.06601v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.17670">arXiv:2312.17670</a> <span> [<a href="https://arxiv.org/pdf/2312.17670">pdf</a>, <a href="https://arxiv.org/format/2312.17670">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</span> </div> </div> <p class="title is-5 mathjax"> Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yang%2C+K">Kaiyuan Yang</a>, <a href="/search/cs?searchtype=author&query=Musio%2C+F">Fabio Musio</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+Y">Yihui Ma</a>, <a href="/search/cs?searchtype=author&query=Juchler%2C+N">Norman Juchler</a>, <a href="/search/cs?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a>, <a href="/search/cs?searchtype=author&query=Al-Maskari%2C+R">Rami Al-Maskari</a>, <a href="/search/cs?searchtype=author&query=H%C3%B6her%2C+L">Luciano H枚her</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H+B">Hongwei Bran Li</a>, <a href="/search/cs?searchtype=author&query=Hamamci%2C+I+E">Ibrahim Ethem Hamamci</a>, <a href="/search/cs?searchtype=author&query=Sekuboyina%2C+A">Anjany Sekuboyina</a>, <a href="/search/cs?searchtype=author&query=Shit%2C+S">Suprosanna Shit</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+H">Houjing Huang</a>, <a href="/search/cs?searchtype=author&query=Prabhakar%2C+C">Chinmay Prabhakar</a>, <a href="/search/cs?searchtype=author&query=de+la+Rosa%2C+E">Ezequiel de la Rosa</a>, <a href="/search/cs?searchtype=author&query=Waldmannstetter%2C+D">Diana Waldmannstetter</a>, <a href="/search/cs?searchtype=author&query=Kofler%2C+F">Florian Kofler</a>, <a href="/search/cs?searchtype=author&query=Navarro%2C+F">Fernando Navarro</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M">Martin Menten</a>, <a href="/search/cs?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Vos%2C+I">Iris Vos</a>, <a href="/search/cs?searchtype=author&query=Ruigrok%2C+Y">Ynte Ruigrok</a>, <a href="/search/cs?searchtype=author&query=Velthuis%2C+B">Birgitta Velthuis</a>, <a href="/search/cs?searchtype=author&query=Kuijf%2C+H">Hugo Kuijf</a>, <a href="/search/cs?searchtype=author&query=H%C3%A4mmerli%2C+J">Julien H盲mmerli</a> , et al. (59 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.17670v3-abstract-short" style="display: inline;"> The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modaliti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.17670v3-abstract-full').style.display = 'inline'; document.getElementById('2312.17670v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.17670v3-abstract-full" style="display: none;"> The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.17670v3-abstract-full').style.display = 'none'; document.getElementById('2312.17670v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">24 pages, 11 figures, 9 tables. Summary Paper for the MICCAI TopCoW 2023 Challenge</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.03804">arXiv:2312.03804</a> <span> [<a href="https://arxiv.org/pdf/2312.03804">pdf</a>, <a href="https://arxiv.org/format/2312.03804">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Meissen%2C+F">Felix Meissen</a>, <a href="/search/cs?searchtype=author&query=Getzner%2C+J">Johannes Getzner</a>, <a href="/search/cs?searchtype=author&query=Ziller%2C+A">Alexander Ziller</a>, <a href="/search/cs?searchtype=author&query=Turgut%2C+%C3%96">脰zg眉n Turgut</a>, <a href="/search/cs?searchtype=author&query=Kaissis%2C+G">Georgios Kaissis</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.03804v2-abstract-short" style="display: inline;"> Unsupervised anomaly detection (UAD) alleviates large labeling efforts by training exclusively on unlabeled in-distribution data and detecting outliers as anomalies. Generally, the assumption prevails that large training datasets allow the training of higher-performing UAD models. However, in this work, we show that UAD with extremely few training samples can already match -- and in some cases eve… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.03804v2-abstract-full').style.display = 'inline'; document.getElementById('2312.03804v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.03804v2-abstract-full" style="display: none;"> Unsupervised anomaly detection (UAD) alleviates large labeling efforts by training exclusively on unlabeled in-distribution data and detecting outliers as anomalies. Generally, the assumption prevails that large training datasets allow the training of higher-performing UAD models. However, in this work, we show that UAD with extremely few training samples can already match -- and in some cases even surpass -- the performance of training with the whole training dataset. Building upon this finding, we propose an unsupervised method to reliably identify prototypical samples to further boost UAD performance. We demonstrate the utility of our method on seven different established UAD benchmarks from computer vision, industrial defect detection, and medicine. With just 25 selected samples, we even exceed the performance of full training in $25/67$ categories in these benchmarks. Additionally, we show that the prototypical in-distribution samples identified by our proposed method generalize well across models and datasets and that observing their sample selection criteria allows for a successful manual selection of small subsets of high-performing samples. Our code is available at https://anonymous.4open.science/r/uad_prototypical_samples/ <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.03804v2-abstract-full').style.display = 'none'; document.getElementById('2312.03804v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.16831">arXiv:2309.16831</a> <span> [<a href="https://arxiv.org/pdf/2309.16831">pdf</a>, <a href="https://arxiv.org/format/2309.16831">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-031-44336-7_1">10.1007/978-3-031-44336-7_1 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Propagation and Attribution of Uncertainty in Medical Imaging Pipelines </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Feiner%2C+L+F">Leonhard F. Feiner</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/cs?searchtype=author&query=Hammernik%2C+K">Kerstin Hammernik</a>, <a href="/search/cs?searchtype=author&query=Hager%2C+P">Paul Hager</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+W">Wenqi Huang</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Braren%2C+R+F">Rickmer F. Braren</a>, <a href="/search/cs?searchtype=author&query=Kaissis%2C+G">Georgios Kaissis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.16831v1-abstract-short" style="display: inline;"> Uncertainty estimation, which provides a means of building explainable neural networks for medical imaging applications, have mostly been studied for single deep learning models that focus on a specific task. In this paper, we propose a method to propagate uncertainty through cascades of deep learning models in medical imaging pipelines. This allows us to aggregate the uncertainty in later stages… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.16831v1-abstract-full').style.display = 'inline'; document.getElementById('2309.16831v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.16831v1-abstract-full" style="display: none;"> Uncertainty estimation, which provides a means of building explainable neural networks for medical imaging applications, have mostly been studied for single deep learning models that focus on a specific task. In this paper, we propose a method to propagate uncertainty through cascades of deep learning models in medical imaging pipelines. This allows us to aggregate the uncertainty in later stages of the pipeline and to obtain a joint uncertainty measure for the predictions of later models. Additionally, we can separately report contributions of the aleatoric, data-based, uncertainty of every component in the pipeline. We demonstrate the utility of our method on a realistic imaging pipeline that reconstructs undersampled brain and knee magnetic resonance (MR) images and subsequently predicts quantitative information from the images, such as the brain volume, or knee side or patient's sex. We quantitatively show that the propagated uncertainty is correlated with input uncertainty and compare the proportions of contributions of pipeline stages to the joint uncertainty measure. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.16831v1-abstract-full').style.display = 'none'; document.getElementById('2309.16831v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.08481">arXiv:2309.08481</a> <span> [<a href="https://arxiv.org/pdf/2309.08481">pdf</a>, <a href="https://arxiv.org/format/2309.08481">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> 3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dima%2C+A+F">Alina F. Dima</a>, <a href="/search/cs?searchtype=author&query=Zimmer%2C+V+A">Veronika A. Zimmer</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H+B">Hongwei Bran Li</a>, <a href="/search/cs?searchtype=author&query=Graf%2C+M">Markus Graf</a>, <a href="/search/cs?searchtype=author&query=Lemke%2C+T">Tristan Lemke</a>, <a href="/search/cs?searchtype=author&query=Raffler%2C+P">Philipp Raffler</a>, <a href="/search/cs?searchtype=author&query=Graf%2C+R">Robert Graf</a>, <a href="/search/cs?searchtype=author&query=Kirschke%2C+J+S">Jan S. Kirschke</a>, <a href="/search/cs?searchtype=author&query=Braren%2C+R">Rickmer Braren</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.08481v1-abstract-short" style="display: inline;"> Automated segmentation of the blood vessels in 3D volumes is an essential step for the quantitative diagnosis and treatment of many vascular diseases. 3D vessel segmentation is being actively investigated in existing works, mostly in deep learning approaches. However, training 3D deep networks requires large amounts of manual 3D annotations from experts, which are laborious to obtain. This is espe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.08481v1-abstract-full').style.display = 'inline'; document.getElementById('2309.08481v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.08481v1-abstract-full" style="display: none;"> Automated segmentation of the blood vessels in 3D volumes is an essential step for the quantitative diagnosis and treatment of many vascular diseases. 3D vessel segmentation is being actively investigated in existing works, mostly in deep learning approaches. However, training 3D deep networks requires large amounts of manual 3D annotations from experts, which are laborious to obtain. This is especially the case for 3D vessel segmentation, as vessels are sparse yet spread out over many slices and disconnected when visualized in 2D slices. In this work, we propose a novel method to segment the 3D peripancreatic arteries solely from one annotated 2D projection per training image with depth supervision. We perform extensive experiments on the segmentation of peripancreatic arteries on 3D contrast-enhanced CT images and demonstrate how well we capture the rich depth information from 2D projections. We demonstrate that by annotating a single, randomly chosen projection for each training sample, we obtain comparable performance to annotating multiple 2D projections, thereby reducing the annotation effort. Furthermore, by mapping the 2D labels to the 3D space using depth information and incorporating this into training, we almost close the performance gap between 3D supervision and 2D supervision. Our code is available at: https://github.com/alinafdima/3Dseg-mip-depth. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.08481v1-abstract-full').style.display = 'none'; document.getElementById('2309.08481v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.02527">arXiv:2309.02527</a> <span> [<a href="https://arxiv.org/pdf/2309.02527">pdf</a>, <a href="https://arxiv.org/format/2309.02527">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> A skeletonization algorithm for gradient-based optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/cs?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a>, <a href="/search/cs?searchtype=author&query=Zimmer%2C+V+A">Veronika A. Zimmer</a>, <a href="/search/cs?searchtype=author&query=Shit%2C+S">Suprosanna Shit</a>, <a href="/search/cs?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/cs?searchtype=author&query=Holland%2C+R">Robbie Holland</a>, <a href="/search/cs?searchtype=author&query=Probst%2C+M">Monika Probst</a>, <a href="/search/cs?searchtype=author&query=Schnabel%2C+J+A">Julia A. Schnabel</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.02527v1-abstract-short" style="display: inline;"> The skeleton of a digital image is a compact representation of its topology, geometry, and scale. It has utility in many computer vision applications, such as image description, segmentation, and registration. However, skeletonization has only seen limited use in contemporary deep learning solutions. Most existing skeletonization algorithms are not differentiable, making it impossible to integrate… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.02527v1-abstract-full').style.display = 'inline'; document.getElementById('2309.02527v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.02527v1-abstract-full" style="display: none;"> The skeleton of a digital image is a compact representation of its topology, geometry, and scale. It has utility in many computer vision applications, such as image description, segmentation, and registration. However, skeletonization has only seen limited use in contemporary deep learning solutions. Most existing skeletonization algorithms are not differentiable, making it impossible to integrate them with gradient-based optimization. Compatible algorithms based on morphological operations and neural networks have been proposed, but their results often deviate from the geometry and topology of the true medial axis. This work introduces the first three-dimensional skeletonization algorithm that is both compatible with gradient-based optimization and preserves an object's topology. Our method is exclusively based on matrix additions and multiplications, convolutional operations, basic non-linear functions, and sampling from a uniform probability distribution, allowing it to be easily implemented in any major deep learning library. In benchmarking experiments, we prove the advantages of our skeletonization algorithm compared to non-differentiable, morphological, and neural-network-based baselines. Finally, we demonstrate the utility of our algorithm by integrating it with two medical image processing applications that use gradient-based optimization: deep-learning-based blood vessel segmentation, and multimodal registration of the mandible in computed tomography and magnetic resonance images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.02527v1-abstract-full').style.display = 'none'; document.getElementById('2309.02527v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at ICCV 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.05764">arXiv:2308.05764</a> <span> [<a href="https://arxiv.org/pdf/2308.05764">pdf</a>, <a href="https://arxiv.org/format/2308.05764">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Unlocking the Diagnostic Potential of ECG through Knowledge Transfer from Cardiac MRI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Turgut%2C+%C3%96">脰zg眉n Turgut</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCller%2C+P">Philip M眉ller</a>, <a href="/search/cs?searchtype=author&query=Hager%2C+P">Paul Hager</a>, <a href="/search/cs?searchtype=author&query=Shit%2C+S">Suprosanna Shit</a>, <a href="/search/cs?searchtype=author&query=Starck%2C+S">Sophie Starck</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/cs?searchtype=author&query=Martens%2C+E">Eimo Martens</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.05764v1-abstract-short" style="display: inline;"> The electrocardiogram (ECG) is a widely available diagnostic tool that allows for a cost-effective and fast assessment of the cardiovascular health. However, more detailed examination with expensive cardiac magnetic resonance (CMR) imaging is often preferred for the diagnosis of cardiovascular diseases. While providing detailed visualization of the cardiac anatomy, CMR imaging is not widely availa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.05764v1-abstract-full').style.display = 'inline'; document.getElementById('2308.05764v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.05764v1-abstract-full" style="display: none;"> The electrocardiogram (ECG) is a widely available diagnostic tool that allows for a cost-effective and fast assessment of the cardiovascular health. However, more detailed examination with expensive cardiac magnetic resonance (CMR) imaging is often preferred for the diagnosis of cardiovascular diseases. While providing detailed visualization of the cardiac anatomy, CMR imaging is not widely available due to long scan times and high costs. To address this issue, we propose the first self-supervised contrastive approach that transfers domain-specific information from CMR images to ECG embeddings. Our approach combines multimodal contrastive learning with masked data modeling to enable holistic cardiac screening solely from ECG data. In extensive experiments using data from 40,044 UK Biobank subjects, we demonstrate the utility and generalizability of our method. We predict the subject-specific risk of various cardiovascular diseases and determine distinct cardiac phenotypes solely from ECG data. In a qualitative analysis, we demonstrate that our learned ECG embeddings incorporate information from CMR image regions of interest. We make our entire pipeline publicly available, including the source code and pre-trained model weights. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.05764v1-abstract-full').style.display = 'none'; document.getElementById('2308.05764v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.00402">arXiv:2308.00402</a> <span> [<a href="https://arxiv.org/pdf/2308.00402">pdf</a>, <a href="https://arxiv.org/format/2308.00402">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Metrics to Quantify Global Consistency in Synthetic Medical Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Scholz%2C+D">Daniel Scholz</a>, <a href="/search/cs?searchtype=author&query=Wiestler%2C+B">Benedikt Wiestler</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.00402v1-abstract-short" style="display: inline;"> Image synthesis is increasingly being adopted in medical image processing, for example for data augmentation or inter-modality image translation. In these critical applications, the generated images must fulfill a high standard of biological correctness. A particular requirement for these images is global consistency, i.e an image being overall coherent and structured so that all parts of the imag… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.00402v1-abstract-full').style.display = 'inline'; document.getElementById('2308.00402v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.00402v1-abstract-full" style="display: none;"> Image synthesis is increasingly being adopted in medical image processing, for example for data augmentation or inter-modality image translation. In these critical applications, the generated images must fulfill a high standard of biological correctness. A particular requirement for these images is global consistency, i.e an image being overall coherent and structured so that all parts of the image fit together in a realistic and meaningful way. Yet, established image quality metrics do not explicitly quantify this property of synthetic images. In this work, we introduce two metrics that can measure the global consistency of synthetic images on a per-image basis. To measure the global consistency, we presume that a realistic image exhibits consistent properties, e.g., a person's body fat in a whole-body MRI, throughout the depicted object or scene. Hence, we quantify global consistency by predicting and comparing explicit attributes of images on patches using supervised trained neural networks. Next, we adapt this strategy to an unlabeled setting by measuring the similarity of implicit image features predicted by a self-supervised trained network. Our results demonstrate that predicting explicit attributes of synthetic images on patches can distinguish globally consistent from inconsistent images. Implicit representations of images are less sensitive to assess global consistency but are still serviceable when labeled data is unavailable. Compared to established metrics, such as the FID, our method can explicitly measure global consistency on a per-image basis, enabling a dedicated analysis of the biological plausibility of single synthetic images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.00402v1-abstract-full').style.display = 'none'; document.getElementById('2308.00402v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.10941">arXiv:2306.10941</a> <span> [<a href="https://arxiv.org/pdf/2306.10941">pdf</a>, <a href="https://arxiv.org/format/2306.10941">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kreitner%2C+L">Linus Kreitner</a>, <a href="/search/cs?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a>, <a href="/search/cs?searchtype=author&query=Rauch%2C+N">Nikolaus Rauch</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+C">Chen Chen</a>, <a href="/search/cs?searchtype=author&query=Hagag%2C+A+M">Ahmed M. Hagag</a>, <a href="/search/cs?searchtype=author&query=Fayed%2C+A+E">Alaa E. Fayed</a>, <a href="/search/cs?searchtype=author&query=Sivaprasad%2C+S">Sobha Sivaprasad</a>, <a href="/search/cs?searchtype=author&query=Rausch%2C+S">Sebastian Rausch</a>, <a href="/search/cs?searchtype=author&query=Weichsel%2C+J">Julian Weichsel</a>, <a href="/search/cs?searchtype=author&query=Menze%2C+B+H">Bjoern H. Menze</a>, <a href="/search/cs?searchtype=author&query=Harders%2C+M">Matthias Harders</a>, <a href="/search/cs?searchtype=author&query=Knier%2C+B">Benjamin Knier</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.10941v2-abstract-short" style="display: inline;"> Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are st… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.10941v2-abstract-full').style.display = 'inline'; document.getElementById('2306.10941v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.10941v2-abstract-full" style="display: none;"> Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.10941v2-abstract-full').style.display = 'none'; document.getElementById('2306.10941v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Currently under review</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.14080">arXiv:2303.14080</a> <span> [<a href="https://arxiv.org/pdf/2303.14080">pdf</a>, <a href="https://arxiv.org/format/2303.14080">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hager%2C+P">Paul Hager</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.14080v3-abstract-short" style="display: inline;"> Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images. In practice, clinicians typically have less data, both in terms of diversity and scale, but still wish to deploy deep learning solutions. Combined with increasing medical dataset sizes and expensive annotation costs, the necessity for unsupervised methods that can pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.14080v3-abstract-full').style.display = 'inline'; document.getElementById('2303.14080v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.14080v3-abstract-full" style="display: none;"> Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images. In practice, clinicians typically have less data, both in terms of diversity and scale, but still wish to deploy deep learning solutions. Combined with increasing medical dataset sizes and expensive annotation costs, the necessity for unsupervised methods that can pretrain multimodally and predict unimodally has risen. To address these needs, we propose the first self-supervised contrastive learning framework that takes advantage of images and tabular data to train unimodal encoders. Our solution combines SimCLR and SCARF, two leading contrastive learning strategies, and is simple and effective. In our experiments, we demonstrate the strength of our framework by predicting risks of myocardial infarction and coronary artery disease (CAD) using cardiac MR images and 120 clinical features from 40,000 UK Biobank subjects. Furthermore, we show the generalizability of our approach to natural images using the DVM car advertisement dataset. We take advantage of the high interpretability of tabular data and through attribution and ablation experiments find that morphometric tabular features, describing size and shape, have outsized importance during the contrastive learning process and improve the quality of the learned embeddings. Finally, we introduce a novel form of supervised contrastive learning, label as a feature (LaaF), by appending the ground truth label as a tabular feature during multimodal pretraining, outperforming all supervised contrastive baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.14080v3-abstract-full').style.display = 'none'; document.getElementById('2303.14080v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in CVPR 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.04525">arXiv:2301.04525</a> <span> [<a href="https://arxiv.org/pdf/2301.04525">pdf</a>, <a href="https://arxiv.org/format/2301.04525">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Holland%2C+R">Robbie Holland</a>, <a href="/search/cs?searchtype=author&query=Leingang%2C+O">Oliver Leingang</a>, <a href="/search/cs?searchtype=author&query=Holmes%2C+C">Christopher Holmes</a>, <a href="/search/cs?searchtype=author&query=Anders%2C+P">Philipp Anders</a>, <a href="/search/cs?searchtype=author&query=Kaye%2C+R">Rebecca Kaye</a>, <a href="/search/cs?searchtype=author&query=Riedl%2C+S">Sophie Riedl</a>, <a href="/search/cs?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a>, <a href="/search/cs?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/cs?searchtype=author&query=Bogunovi%C4%87%2C+H">Hrvoje Bogunovi膰</a>, <a href="/search/cs?searchtype=author&query=Schmidt-Erfurth%2C+U">Ursula Schmidt-Erfurth</a>, <a href="/search/cs?searchtype=author&query=Fritsche%2C+L">Lars Fritsche</a>, <a href="/search/cs?searchtype=author&query=Scholl%2C+H+P+N">Hendrik P. N. Scholl</a>, <a href="/search/cs?searchtype=author&query=Sivaprasad%2C+S">Sobha Sivaprasad</a>, <a href="/search/cs?searchtype=author&query=Lotery%2C+A+J">Andrew J. Lotery</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.04525v2-abstract-short" style="display: inline;"> Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories and are unable to predict future disease progression. It is widely believed that this is due to their focus on a single point in time, disregarding the dynamic nature of the disease. In this work, we pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.04525v2-abstract-full').style.display = 'inline'; document.getElementById('2301.04525v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.04525v2-abstract-full" style="display: none;"> Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories and are unable to predict future disease progression. It is widely believed that this is due to their focus on a single point in time, disregarding the dynamic nature of the disease. In this work, we present the first method to automatically discover biomarkers that capture temporal dynamics of disease progression. Our method represents patient time series as trajectories in a latent feature space built with contrastive learning. Then, individual trajectories are partitioned into atomic sub-sequences that encode transitions between disease states. These are clustered using a newly introduced distance metric. In quantitative experiments we found our method yields temporal biomarkers that are predictive of conversion to late AMD. Furthermore, these clusters were highly interpretable to ophthalmologists who confirmed that many of the clusters represent dynamics that have previously been linked to the progression of AMD, even though they are currently not included in any clinical grading system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.04525v2-abstract-full').style.display = 'none'; document.getElementById('2301.04525v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to MICCAI2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.16053">arXiv:2210.16053</a> <span> [<a href="https://arxiv.org/pdf/2210.16053">pdf</a>, <a href="https://arxiv.org/format/2210.16053">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kreitner%2C+L">Linus Kreitner</a>, <a href="/search/cs?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.16053v3-abstract-short" style="display: inline;"> Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex. In this work, we investigate a novel method for automated DR grading based on optical coherence tomography angiography (OCTA) images. Our work combines OCTA scans with their vessel segmentations, which then serve as inputs to task specific networks for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.16053v3-abstract-full').style.display = 'inline'; document.getElementById('2210.16053v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.16053v3-abstract-full" style="display: none;"> Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex. In this work, we investigate a novel method for automated DR grading based on optical coherence tomography angiography (OCTA) images. Our work combines OCTA scans with their vessel segmentations, which then serve as inputs to task specific networks for lesion segmentation, image quality assessment and DR grading. For this, we generate synthetic OCTA images to train a segmentation network that can be directly applied on real OCTA data. We test our approach on MICCAI 2022's DR analysis challenge (DRAC). In our experiments, the proposed method performs equally well as the baseline model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.16053v3-abstract-full').style.display = 'none'; document.getElementById('2210.16053v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submission for MICCAI 2022 Diabetic Retinopathy Analysis Challenge (DRAC) Proceedings, DOI: 10.5281/zenodo.6362349</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.02529">arXiv:2208.02529</a> <span> [<a href="https://arxiv.org/pdf/2208.02529">pdf</a>, <a href="https://arxiv.org/format/2208.02529">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Metadata-enhanced contrastive learning from retinal optical coherence tomography images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Holland%2C+R">Robbie Holland</a>, <a href="/search/cs?searchtype=author&query=Leingang%2C+O">Oliver Leingang</a>, <a href="/search/cs?searchtype=author&query=Bogunovi%C4%87%2C+H">Hrvoje Bogunovi膰</a>, <a href="/search/cs?searchtype=author&query=Riedl%2C+S">Sophie Riedl</a>, <a href="/search/cs?searchtype=author&query=Fritsche%2C+L">Lars Fritsche</a>, <a href="/search/cs?searchtype=author&query=Prevost%2C+T">Toby Prevost</a>, <a href="/search/cs?searchtype=author&query=Scholl%2C+H+P+N">Hendrik P. N. Scholl</a>, <a href="/search/cs?searchtype=author&query=Schmidt-Erfurth%2C+U">Ursula Schmidt-Erfurth</a>, <a href="/search/cs?searchtype=author&query=Sivaprasad%2C+S">Sobha Sivaprasad</a>, <a href="/search/cs?searchtype=author&query=Lotery%2C+A+J">Andrew J. Lotery</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.02529v3-abstract-short" style="display: inline;"> Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-efficient downstream image analysis. However, the direct application of conventional contrastive methods to medical datasets introduces two domain-spe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.02529v3-abstract-full').style.display = 'inline'; document.getElementById('2208.02529v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.02529v3-abstract-full" style="display: none;"> Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-efficient downstream image analysis. However, the direct application of conventional contrastive methods to medical datasets introduces two domain-specific issues. Firstly, several image transformations which have been shown to be crucial for effective contrastive learning do not translate from the natural image to the medical image domain. Secondly, the assumption made by conventional methods, that any two images are dissimilar, is systematically misleading in medical datasets depicting the same anatomy and disease. This is exacerbated in longitudinal image datasets that repeatedly image the same patient cohort to monitor their disease progression over time. In this paper we tackle these issues by extending conventional contrastive frameworks with a novel metadata-enhanced strategy. Our approach employs widely available patient metadata to approximate the true set of inter-image contrastive relationships. To this end we employ records for patient identity, eye position (i.e. left or right) and time series information. In experiments using two large longitudinal datasets containing 170,427 retinal OCT images of 7,912 patients with age-related macular degeneration (AMD), we evaluate the utility of using metadata to incorporate the temporal dynamics of disease progression into pretraining. Our metadata-enhanced approach outperforms both standard contrastive methods and a retinal image foundation model in five out of six image-level downstream tasks related to AMD. Due to its modularity, our method can be quickly and cost-effectively tested to establish the potential benefits of including available metadata in contrastive pretraining. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.02529v3-abstract-full').style.display = 'none'; document.getElementById('2208.02529v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.11102">arXiv:2207.11102</a> <span> [<a href="https://arxiv.org/pdf/2207.11102">pdf</a>, <a href="https://arxiv.org/format/2207.11102">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/cs?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a>, <a href="/search/cs?searchtype=author&query=Dima%2C+A">Alina Dima</a>, <a href="/search/cs?searchtype=author&query=Menze%2C+B+H">Bjoern H. Menze</a>, <a href="/search/cs?searchtype=author&query=Knier%2C+B">Benjamin Knier</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2207.11102v1-abstract-short" style="display: inline;"> Optical coherence tomography angiography (OCTA) can non-invasively image the eye's circulatory system. In order to reliably characterize the retinal vasculature, there is a need to automatically extract quantitative metrics from these images. The calculation of such biomarkers requires a precise semantic segmentation of the blood vessels. However, deep-learning-based methods for segmentation mostl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.11102v1-abstract-full').style.display = 'inline'; document.getElementById('2207.11102v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.11102v1-abstract-full" style="display: none;"> Optical coherence tomography angiography (OCTA) can non-invasively image the eye's circulatory system. In order to reliably characterize the retinal vasculature, there is a need to automatically extract quantitative metrics from these images. The calculation of such biomarkers requires a precise semantic segmentation of the blood vessels. However, deep-learning-based methods for segmentation mostly rely on supervised training with voxel-level annotations, which are costly to obtain. In this work, we present a pipeline to synthesize large amounts of realistic OCTA images with intrinsically matching ground truth labels; thereby obviating the need for manual annotation of training data. Our proposed method is based on two novel components: 1) a physiology-based simulation that models the various retinal vascular plexuses and 2) a suite of physics-based image augmentations that emulate the OCTA image acquisition process including typical artifacts. In extensive benchmarking experiments, we demonstrate the utility of our synthetic data by successfully training retinal vessel segmentation algorithms. Encouraged by our method's competitive quantitative and superior qualitative performance, we believe that it constitutes a versatile tool to advance the quantitative analysis of OCTA images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.11102v1-abstract-full').style.display = 'none'; document.getElementById('2207.11102v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at MICCAI 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.04550">arXiv:2205.04550</a> <span> [<a href="https://arxiv.org/pdf/2205.04550">pdf</a>, <a href="https://arxiv.org/format/2205.04550">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> </div> </div> <p class="title is-5 mathjax"> A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/cs?searchtype=author&query=Rosier%2C+M">Marcel Rosier</a>, <a href="/search/cs?searchtype=author&query=Zimmer%2C+L">Lucas Zimmer</a>, <a href="/search/cs?searchtype=author&query=Kofler%2C+F">Florian Kofler</a>, <a href="/search/cs?searchtype=author&query=Shit%2C+S">Suprosanna Shit</a>, <a href="/search/cs?searchtype=author&query=Paetzold%2C+J">Johannes Paetzold</a>, <a href="/search/cs?searchtype=author&query=Scibilia%2C+K">Kevin Scibilia</a>, <a href="/search/cs?searchtype=author&query=Maechler%2C+L">Leon Maechler</a>, <a href="/search/cs?searchtype=author&query=Franitza%2C+K">Katharina Franitza</a>, <a href="/search/cs?searchtype=author&query=Amiranashvili%2C+T">Tamaz Amiranashvili</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/cs?searchtype=author&query=Metz%2C+M">Marie Metz</a>, <a href="/search/cs?searchtype=author&query=Conjeti%2C+S">Sailesh Conjeti</a>, <a href="/search/cs?searchtype=author&query=Wiestler%2C+B">Benedikt Wiestler</a>, <a href="/search/cs?searchtype=author&query=Menze%2C+B">Bjoern Menze</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.04550v3-abstract-short" style="display: inline;"> Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational inference based methods. Both approaches aim to identify a set of free physical mo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.04550v3-abstract-full').style.display = 'inline'; document.getElementById('2205.04550v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.04550v3-abstract-full" style="display: none;"> Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational inference based methods. Both approaches aim to identify a set of free physical model parameters that results in a simulation best matching an empirical observation. When applied to brain tumor modeling, one of the instances of image-based model personalization in medical image computing, the overarching drawback of the methods is the time complexity for finding such a set. In a clinical setting with limited time between imaging and diagnosis or even intervention, this time complexity may prove critical. As the history of quantitative science is the history of compression, we align in this paper with the historical tendency and propose a method compressing complex traditional strategies for solving an inverse problem into a simple database query task. We evaluated different ways of performing the database query task assessing the trade-off between accuracy and execution time. On the exemplary task of brain tumor growth modeling, we prove that the proposed method achieves one order speed-up compared to existing approaches for solving the inverse problem. The resulting compute time offers critical means for relying on more complex and, hence, realistic models, for integrating image preprocessing and inverse modeling even deeper, or for implementing the current model into a clinical workflow. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.04550v3-abstract-full').style.display = 'none'; document.getElementById('2205.04550v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.04090">arXiv:2111.04090</a> <span> [<a href="https://arxiv.org/pdf/2111.04090">pdf</a>, <a href="https://arxiv.org/format/2111.04090">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/cs?searchtype=author&query=Scibilia%2C+K">Kevin Scibilia</a>, <a href="/search/cs?searchtype=author&query=Franitza%2C+K">Katharina Franitza</a>, <a href="/search/cs?searchtype=author&query=Steinbauer%2C+F">Felix Steinbauer</a>, <a href="/search/cs?searchtype=author&query=Shit%2C+S">Suprosanna Shit</a>, <a href="/search/cs?searchtype=author&query=Zimmer%2C+L">Lucas Zimmer</a>, <a href="/search/cs?searchtype=author&query=Lipkova%2C+J">Jana Lipkova</a>, <a href="/search/cs?searchtype=author&query=Kofler%2C+F">Florian Kofler</a>, <a href="/search/cs?searchtype=author&query=Paetzold%2C+J">Johannes Paetzold</a>, <a href="/search/cs?searchtype=author&query=Canalini%2C+L">Luca Canalini</a>, <a href="/search/cs?searchtype=author&query=Waldmannstetter%2C+D">Diana Waldmannstetter</a>, <a href="/search/cs?searchtype=author&query=Menten%2C+M">Martin Menten</a>, <a href="/search/cs?searchtype=author&query=Metz%2C+M">Marie Metz</a>, <a href="/search/cs?searchtype=author&query=Wiestler%2C+B">Benedikt Wiestler</a>, <a href="/search/cs?searchtype=author&query=Menze%2C+B">Bjoern Menze</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2111.04090v3-abstract-short" style="display: inline;"> Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.04090v3-abstract-full').style.display = 'inline'; document.getElementById('2111.04090v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.04090v3-abstract-full" style="display: none;"> Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.04090v3-abstract-full').style.display = 'none'; document.getElementById('2111.04090v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div 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