While 3D foundational models have shown promise for promptable segmentation of medical volumes, their robustness to imprecise prompts remains under-explored. In this work, we aim to address this gap by systematically studying the effect of various controlled perturbations of dense visual prompts, that closely mimic real-world imprecision. By conducting experiments with two recent foundational models on a multi-organ abdominal segmentation task, we reveal several facets of promptable medical segmentation, especially pertaining to reliance on visual shape and spatial cues, and the extent of resilience of models towards certain perturbations. Codes are available at: https://github.com/ucsdbiag/Prompt-Robustness-MedSegFMs
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；NVIDIA（英伟达）
Textures and edges contribute different information to image recognition. Edges and boundaries encode shape information, while textures manifest the appearance of regions. Despite the success of Convolutional Neural Networks (CNNs) in computer vision and medical image analysis applications, predominantly only texture abstractions are learned, which often leads to imprecise boundary delineations. In medical imaging, expert manual segmentation often relies on organ boundaries; for example, to manually segment a liver, a medical practitioner usually identifies edges first and subsequently fills i…
Medical Image Segmentation with Belief Function Theory and Deep Learning
University of Technology of Compiègne（贡比涅技术大学）；Heudiasyc Laboratory（欧洲计算机科学与自动化研究院）；University of Paris-Saclay（巴黎萨克雷大学）；University of Artois（阿图瓦大学）；ENSMM School（国立高等机械与微技术学院）；University of Rouen（鲁昂大学）
Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
Shanghai AI Laboratory（上海人工智能实验室）；Sichuan University（四川大学）；Monash University（莫纳什大学）；East China Normal University（华东师范大学）；Shanghai Jiao Tong University（上海交通大学）；Xinjiang University（新疆大学）
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Artificial Intelligence Institute, Shanghai University（上海大学人工智能研究院）；Department of Computer and Data Sciences, Case Western Reserve University（凯斯西储大学计算机与数据科学系）；Department of Biomedical Engineering, Case Western Reserve University（凯斯西储大学生物医学工程系）
Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所）；Institute of Image Analysis and Computer Vision, Faculty of Informatics and Data Science, University of Regensburg（雷根斯堡大学信息学与数据科学学院图像分析与计算机视觉研究所）；Institute for Pathology, Hannover Medical School（汉诺威医学院病理学研究所）；Institute for Neuropathology, University Clinic Freiburg（弗莱堡大学医院神经病理学研究所）；Institute for Experimental Molecular Imaging, RWTH Aachen University（亚琛工业大学实验分子影像学研究所）
Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty
National Key Laboratory of Fundamental Science on Synthetic Vision, the College of Computer Science, Sichuan University（国家基础科学合成视觉重点实验室、计算机科学学院、四川大学）；Department of Radiology, West China Hospital, Sichuan University（放射科、西昌医院、四川大学）；Saw Swee Hock School of Public Health, National University of Singapore（Saw Swee Hock 公共卫生学院、新加坡国立大学）；Department of Mathematics, Sichuan University（数学系、四川大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（高性能计算研究所（IHPC）、科学、技术与研究局（A*STAR））；Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore（创新与精准眼健康中心、 Yong Loo Lin 医学院、新加坡国立大学）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡眼研究所、新加坡国家眼科中心）
On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation
University of Stuttgart（斯图加特大学）；IMPRS for Intelligent Systems（智能系统马克斯普朗克研究学校（IMPRS））；German Research Center for Artificial Intelligence（德国人工智能研究中心）；University of Science - VNUHCM（胡志明市国家大学科学大学）；Singapore Management University（新加坡管理大学）；University of Arkansas（阿肯色大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of California San Diego（加利福尼亚大学圣地亚哥分校）；MBZUAI（Mohamed bin Zayed 人工智能大学）；Oldenburg University（奥尔登堡大学）
Leveraging Causal Reasoning Method for Explaining Medical Image Segmentation Models
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen University of Advanced Technology（深圳先进技术大学）；Shenzhen Polytechnic University（深圳职业技术大学）；University of Macau（澳门大学）；Southern University of Science and Technology（南方科技大学）
I-MedSAM: Implicit Medical Image Segmentation with Segment Anything
Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Beihang University（北京航空航天大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
Intelligent Communication Mixture-of-Experts Boosted-Medical Image Segmentation Foundation Model
College of Optoelectronics Engineering, Chongqing University, Chongqing, China；Center of Digital Dentistry, Peking University School；Hospital of Stomatology \& NHC Key Laboratory of Digital Stomatology, Beijing, China
SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；College of Computer Science, East China Normal University（华东师范大学计算机科学学院）；Department of Data Science and AI, Faculty of IT, Monash University（莫纳什大学信息技术学院数据科学与人工智能系）；College of Computer Science, Sichuan University（四川大学计算机学院）
Concept-to-Pixel: Prompt-Free Universal Medical Image Segmentation
School of Biomedical Engineering, Division of Life Sciences；Medicine, University of Science；Technology of China (USTC), Hefei, Anhui 230026, China Center for Medical Imaging, Robotics, Analytic Computing \& Learning (MIRACLE), Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu 215123, China Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology, Suzhou Jiangsu, 215123, China State Key Laboratory of Precision
Landmark localization is a challenging problem in computer vision with a multitude of applications. Recent deep learning based methods have shown improved results by regressing likelihood maps instead of regressing the coordinates directly. However, setting the precision of these regression targets during the training is a cumbersome process since it creates a trade-off between trainability vs localization accuracy. Using precise targets introduces a significant sampling bias and hence makes the training more difficult, whereas using imprecise targets results in inaccurate landmark detectors. …
Zhejiang University（浙江大学）；University of Notre Dame（圣母大学）
In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities. Recently, deep learning based frameworks have attained expert-level performance on medical image interpretation, which can be attributed partially to large amounts of accurate annotations. However, manually annotating massive amounts of medical images is impractical, while automatic annotation is fast but imprecise (possibly introducing corrupted labels). In this work, we propose a new regularization approach, …
Beyond the LUMIR challenge: The pathway to foundational registration models
The Russell H. Morgan Department of Radiology（Russell H. Morgan放射科）；Radiological Science, Johns Hopkins Medical School（约翰霍普金斯医学院放射科学）；Department of Computer Science, Aalto University（阿尔托大学计算机科学系）；Cornell University（康奈尔大学）；Canon Medical Systems (China) Co. Ltd.（佳能医疗系统（中国）有限公司）；School of Biomedical Engineering, Shenzhen University Medical School（深圳大学医学院生物医学工程学院）；Department of Imaging Physics, Delft University of Technology（代尔夫特理工大学成像物理系）；Technical University of Munich（慕尼黑技术大学）；Radboud University Medical Center（拉德伯德大学医学中心）；Inria, Paris, France（法国巴黎Inria）
LMU Munich（慕尼黑大学）；University Hospital, LMU Munich（慕尼黑大学附属医院）；Munich Center for Machine Learning（慕尼黑机器学习中心）
While recent advances in large-scale foundational models show promising results, their application to the medical domain has not yet been explored in detail. In this paper, we progress into the realms of large-scale modeling in medical synthesis by proposing Cheff - a foundational cascaded latent diffusion model, which generates highly-realistic chest radiographs providing state-of-the-art quality on a 1-megapixel scale. We further propose MaCheX, which is a unified interface for public chest datasets and forms the largest open collection of chest X-rays up to date. With Cheff conditioned on r…
University of Cambridge（剑桥大学）；Royal Brompton Hospital（皇家布朗普顿医院）；Imperial College London（帝国理工学院）
Single image super-resolution (SISR) aims to obtain a high-resolution output from one low-resolution image. Currently, deep learning-based SISR approaches have been widely discussed in medical image processing, because of their potential to achieve high-quality, high spatial resolution images without the cost of additional scans. However, most existing methods are designed for scale-specific SR tasks and are unable to generalise over magnification scales. In this paper, we propose an approach for medical image arbitrary-scale super-resolution (MIASSR), in which we couple meta-learning with gen…
SAM3D: Segment Anything Model in Volumetric Medical Images
University of Arkansas（阿肯色大学）；University of Science, Vietnam National University（越南国家大学科学大学）；John von Neumann Institute, Vietnam National University（越南国家大学约翰·冯·诺依曼研究所）；West Virginia University（西弗吉尼亚大学）；University of Arkansas for Medical Sciences（阿肯色医科大学）
Lightweight Medical Image Restoration via Integrating Reliable Lesion-Semantic Driven Prior
University of Electronic Science and Technology of China（电子科技大学）；City University of Hong Kong（香港城市大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Shenzhen Institute For Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳研究院）
Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis
School of Biomedical Engineering（生物医学工程学院）；University of British Columbia（不列颠哥伦比亚大学）；Tarbiat Modares University（塔比亚特·莫达雷斯大学）；University of Regensburg（雷根斯堡大学）；South Dakota State University（南达科他州立大学）；RWTH Aachen University（亚琛工业大学）；Lunit Inc.（Lunit公司）；Northwestern University（西北大学）；Fraunhofer Institute for Digital Medicine（弗劳恩霍夫数字医学研究院）
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；Beijing Neurosurgical Institute（北京神经外科研究所）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学附属北京天坛医院）；Sorbonne Université（索邦大学）；Institut du Cerveau – Paris Brain Institute - ICM（巴黎脑研究所（ICM））；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Hôpital de la Pitié-Salpêtrière（皮蒂-萨尔皮特里耶医院）
