This work introduces a new framework, ProtoSAM, for one-shot medical image segmentation. It combines the use of prototypical networks, known for few-shot segmentation, with SAM - a natural image foundation model. The method proposed creates an initial coarse segmentation mask using the ALPnet prototypical network, augmented with a DINOv2 encoder. Following the extraction of an initial mask, prompts are extracted, such as points and bounding boxes, which are then input into the Segment Anything Model (SAM). State-of-the-art results are shown on several medical image datasets and demonstrate automated segmentation capabilities using a single image example (one shot) with no need for fine-tuning of the foundation model. Our code is available at: https://github.com/levayz/ProtoSAM
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）
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
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）
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）
Center for Artificial Intelligence in Medicine and Imaging（医学与成像人工智能中心）；Stanford University（斯坦福大学）
Multi-modal foundation models are typically trained on millions of pairs of natural images and text captions, frequently obtained through web-crawling approaches. Although such models depict excellent generative capabilities, they do not typically generalize well to specific domains such as medical images that have fundamentally shifted distributions compared to natural images. Building generative models for medical images that faithfully depict clinical context may help alleviate the paucity of healthcare datasets. Thus, in this study, we seek to research and expand the representational capab…
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
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）
Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision
South Dakota State University（南达科他州立大学）；RWTH Aachen University（亚琛工业大学）；University of Kentucky（肯塔基大学）；University of Regensburg（雷根斯堡大学）；Iran University of Science and Technology（伊朗科技大学）；Imperial College London（伦敦帝国理工学院）
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）
Label-Free Foundational Model Selection for Medical Image Classification under Distribution Shift via Pseudo Label Discrepancy
Universidad de Buenos Aires（布宜诺斯艾利斯大学）；CONICET（阿根廷国家科学技术研究委员会）；Universidad Nacional del Litoral（国立 littoral 大学）；Research Institute for Signals, Systems and Computational Intelligence（信号、系统与计算智能研究所）
An Empirical Study on the Fairness of Foundation Models for Multi-Organ Image Segmentation
Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Brigham and Women’s Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；MR Business Unit, Philips Healthcare（飞利浦医疗磁共振事业部）；Simens Healthineers Ltd., China（西门子医疗中国有限公司）；Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院数字医学研究中心）
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…
TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation
Sichuan University（四川大学）；Case Western Reserve University（凯斯西储大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Urgench State University（乌尔根奇国立大学）；Gansu Agricultural University（甘肃农业大学）
Generative deep learning for foundational video translation in ultrasound
Department of Medicine, Division of Cardiology（医学系，心脏病科）；Bakar Computational Health Sciences Institute（Bakar计算健康科学研究所）；University of California, San Francisco（加州大学旧金山分校）；UCSF-UC Berkeley Joint Program in Computational Precision Health（UCSF-伯克利计算精准健康联合项目）；Department of Radiology, Center for Intelligent Imaging（放射科，智能成像中心）
Exploring scalable medical image encoders beyond text supervision
Health Futures, Microsoft Research（微软研究院健康未来部门）；Microsoft Health and Life Sciences（微软健康与生命科学部门）；Department of Radiology, University of Cambridge（剑桥大学放射学系）；Cambridge University Hospitals NHS Foundation Trust（剑桥大学医院NHS基金会信托）；Microsoft Azure AI（微软Azure人工智能部门）
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）
A Foundational Generative Model for Breast Ultrasound Image Analysis
Peking University（北京大学）；Peking University Cancer Hospital & Institute（北京大学肿瘤医院暨研究所）；Peking Union Medical College Hospital（北京协和医院）；Cancer Hospital, Chinese Academy of Medical Sciences（中国医学科学院肿瘤医院）；Stanford University（斯坦福大学）；Yizhun Medical AI Co., Ltd.（医准智能医疗科技有限公司）
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）
