医学 AI

SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Jin Ye, Junlong Cheng, Jianpin Chen, Zhongying Deng, Tianbin Li, Haoyu Wang, Yanzhou Su, Ziyan Huang, Jilong Chen, Lei Jiang, Hui Sun, Min Zhu, Shaoting Zhang, Junjun He, Yu Qiao

专题命中 医学数据与评测 ：medical image(abstract)；diagnosis(abstract)；分类 cs.CV、eess.IV

Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data

Pramit Saha, Divyanshu Mishra, J. Alison Noble

专题命中 医学数据与评测 ：medical image(abstract)；biomedical(abstract)；分类 cs.CV、cs.LG

Comments Published in MICCAI 2023 with early acceptance and selected as 1 of the top 20 poster highlights under the category: Which work has the potential to impact other applications of AI and CV

ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images

An Zeng, Chunbiao Wu, Meiping Huang, Jian Zhuang, Shanshan Bi, Dan Pan, Najeeb Ullah, Kaleem Nawaz Khan, Tianchen Wang, Yiyu Shi, Xiaomeng Li, Guisen Lin, Xiaowei Xu

专题命中 医学数据与评测 ：CT(abstract)；diagnosis(abstract)；分类 cs.LG、eess.IV

Comments 17 pages, 12 figures, 4 tables

Journal ref Computerized Medical Imaging and Graphics, 2023

Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery

Cristina-Madalina Dragan, Muhammad Muneeb Saad, Mubashir Husain Rehmani, Ruairi O'Reilly

专题命中 医学数据与评测 ：medical image(abstract)；biomedical(abstract)；分类 cs.CV、eess.IV

Comments 29 Pages, 8 Figures, submitted to MEDAL23: Advances in Deep Generative Models for Medical Artificial Intelligence (Springer Nature series)

Secure & Private Federated Neuroimaging

Dimitris Stripelis, Umang Gupta, Hamza Saleem, Nikhil Dhinagar, Tanmay Ghai, Rafael Chrysovalantis Anastasiou, Armaghan Asghar, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite

专题命中 医学数据与评测 ：MRI(abstract)；biomedical(abstract)；分类 cs.CV、cs.LG

Comments 18 pages, 13 figures, 2 tables

Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge

Jun Ma, Yao Zhang, Song Gu, Cheng Ge, Shihao Ma, Adamo Young, Cheng Zhu, Kangkang Meng, Xin Yang, Ziyan Huang, Fan Zhang, Wentao Liu, YuanKe Pan, Shoujin Huang, Jiacheng Wang, Mingze Sun, Weixin Xu, Dengqiang Jia, Jae Won Choi, Natália Alves, Bram de Wilde, Gregor Koehler, Yajun Wu, Manuel Wiesenfarth, Qiongjie Zhu, Guoqiang Dong, Jian He, the FLARE Challenge Consortium, Bo Wang

专题命中 医学数据与评测 ：CT(abstract)；diagnosis(abstract)；分类 cs.CV、eess.IV

Comments MICCAI FLARE22: https://flare22.grand-challenge.org/

Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction

Wangduo Xie, Matthew B. Blaschko

专题命中 医学数据与评测 ：CT(abstract)；diagnosis(abstract)；分类 cs.CV、eess.IV

A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip Arthroplasty

Kaidong Zhang, Ziyang Gan, Dong Liu, Xifu Shang

专题命中 医学数据与评测 ：medical image(abstract)；CT(abstract)；分类 cs.CV、eess.IV

Comments 16 pages, 17 figures

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni, Zhang Li, Tao Tan, Abhir Bhalerao, Jiabo Ma, Jiamei Sun, Johnathan Pocock, Josien P. W. Pluim, Navid Alemi Koohbanani, Raja Muhammad Saad Bashir, Shan E Ahmed Raza, Sibo Liu, Simon Graham, Suzanne Wetstein, Syed Ali Khurram, Thomas Watson, Nasir Rajpoot, Mitko Veta, Francesco Ciompi

专题命中 医学数据与评测 ：medical image(abstract)；pathology(abstract)；分类 cs.CV、eess.IV

Comments will be sumitted to IEEE-JBHI

Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing

Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

专题命中 医学数据与评测 ：medical image(abstract)；diagnosis(abstract)；分类 cs.CV、cs.LG

Comments Published as a journal paper at Elsevier CMPB

Direct evaluation of progression or regression of disease burden in brain metastatic disease with Deep Neuroevolution

Joseph Stember, Robert Young, Hrithwik Shalu

专题命中 医学数据与评测 ：MRI(abstract)；radiology(abstract)；分类 cs.CV、cs.LG

MR-Contrast-Aware Image-to-Image Translations with Generative Adversarial Networks

Jonas Denck, Jens Guehring, Andreas Maier, Eva Rothgang

专题命中 医学数据与评测 ：MRI(abstract)；diagnosis(abstract)；分类 cs.CV、eess.IV

Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset

Yulei Qin, Yun Gu, Hanxiao Zhang, Jie Yang, Lihui Wang, Zhexin Wang, Feng Yao, Yue-Min Zhu

专题命中 医学数据与评测 ：CT(abstract)；diagnosis(abstract)；分类 cs.CV、eess.IV

Comments 33 pages, 3 figures, Submitted for review

Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease

Martin Dyrba, Moritz Hanzig, Slawek Altenstein, Sebastian Bader, Tommaso Ballarini, Frederic Brosseron, Katharina Buerger, Daniel Cantré, Peter Dechent, Laura Dobisch, Emrah Düzel, Michael Ewers, Klaus Fliessbach, Wenzel Glanz, John-Dylan Haynes, Michael T. Heneka, Daniel Janowitz, Deniz B. Keles, Ingo Kilimann, Christoph Laske, Franziska Maier, Coraline D. Metzger, Matthias H. Munk, Robert Perneczky, Oliver Peters, Lukas Preis, Josef Priller, Boris Rauchmann, Nina Roy, Klaus Scheffler, Anja Schneider, Björn H. Schott, Annika Spottke, Eike J. Spruth, Marc-André Weber, Birgit Ertl-Wagner, Michael Wagner, Jens Wiltfang, Frank Jessen, Stefan J. Teipel

专题命中 医学数据与评测 ：MRI(abstract)；diagnosis(abstract)；分类 cs.CV、eess.IV

Comments 24 pages, 9 figures/tables, supplementary material, source code available on GitHub

Mixing-AdaSIN: Constructing a De-biased Dataset using Adaptive Structural Instance Normalization and Texture Mixing

Myeongkyun Kang, Philip Chikontwe, Miguel Luna, Kyung Soo Hong, June Hong Ahn, Sang Hyun Park

专题命中 医学数据与评测 ：medical image(abstract)；CT(abstract)；分类 cs.CV、eess.IV

Noise2Stack: Improving Image Restoration by Learning from Volumetric Data

Mikhail Papkov, Kenny Roberts, Lee Ann Madissoon, Omer Bayraktar, Dmytro Fishman, Kaupo Palo, Leopold Parts

专题命中 医学数据与评测 ：medical image(abstract)；biomedical(abstract)；分类 cs.CV、eess.IV

BIRL: Benchmark on Image Registration methods with Landmark validation

Jiri Borovec

专题命中 医学数据与评测 ：pathology(abstract)；biomedical(abstract)；分类 cs.CV、eess.IV

Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks

Diego Perna, Andrea Tagarelli

专题命中 医学数据与评测 ：pathology(abstract)；diagnosis(abstract)；分类 cs.LG、eess.SP

Comments Paper accepted for publication with Procs. of the 32th IEEE CBMS International Symposium on Computer-Based Medical Systems (CBMS 2019)

Automated Computer Evaluation of Acute Ischemic Stroke and Large Vessel Occlusion

Jia You, Philip L. H. Yu, Anderson C. O. Tsang, Eva L. H. Tsui, Pauline P. S. Woo, Gilberto K. K. Leung

专题命中 医学数据与评测 ：CT(abstract)；diagnosis(abstract)；分类 cs.LG、eess.IV

LiMT: A Multi-task Liver Image Benchmark Dataset

LiMT：一个多任务肝脏图像基准数据集

Zhe Liu, Kai Han, Siqi Ma, Yan Zhu, Jun Chen, Chongwen Lyu, Xinyi Qiu, Chengxuan Qian, Yuqing Song, Yi Liu, Liyuan Tian, Yang Ji, Yuefeng Li

机构 * School of Computer Science and Communication Engineering, Jiangsu University（江苏大学计算机科学与通信工程学院） ； Department of Imaging, Affiliated Hospital of Jiangsu University（江苏大学附属医院影像科） ； Department of Information, Affiliated Hospital of Jiangsu University（江苏大学附属医院信息科） ； Medical College of Jiangsu University（江苏大学医学院） 纠错

专题命中 医学数据与评测 ：CT(abstract)；diagnosis(abstract)；分类 cs.CV；biomedical(comments)

AI总结 本文提出LiMT多任务肝脏图像数据集，用于肝部和肿瘤分割、多标签病变分类及病变检测，旨在探索任务间相关性并提升CAD技术发展。

Comments IEEE Journal of Biomedical and Health Informatics

Scaling Neuroscience Research using Federated Learning

Dimitris Stripelis, Jose Luis Ambite, Pradeep Lam, Paul Thompson

专题命中 医学数据与评测 ：biomedical(abstract,comments)；MRI(abstract)；分类 cs.LG

Comments To appear at IEEE International Symposium on Biomedical Imaging 2021 (ISBI 2021)

AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

Huazhu Fu, Fei Li, Xu Sun, Xingxing Cao, Jingan Liao, Jose Ignacio Orlando, Xing Tao, Yuexiang Li, Shihao Zhang, Mingkui Tan, Chenglang Yuan, Cheng Bian, Ruitao Xie, Jiongcheng Li, Xiaomeng Li, Jing Wang, Le Geng, Panming Li, Huaying Hao, Jiang Liu, Yan Kong, Yongyong Ren, Hrvoje Bogunovic, Xiulan Zhang, Yanwu Xu

专题命中 医学数据与评测 ：medical image(abstract,comments)；diagnosis(abstract)；分类 cs.CV

Comments Accepted to Medical Image Analysis (MedIA). AGE Challenge website at: https://age.grand-challenge.org

MARC v1: An Open-Source Multi-Agent Framework for Clinical AI Reasoning and Coordination

MARC v1：一个用于临床AI推理与协作的开源多智能体框架

Saisha Shetty, Satvik Tripathi, Austin Lin, Colin Zhao, Theodore Kim, Don Enwerem, Jacinta Arnold, Shahriar Faghani, Tessa S Cook

专题命中 医学数据与评测 ：clinical AI(title)

AI总结 该研究提出开源多智能体框架MARC v1，以确定性多智能体编排替代整体式LLM提示用于临床推理，含角色专业化智能体与分解器模块，支持多部署方式，具备模型无关、可解释等特性。

Comments 13 pages, 4 figures

Probe to Generate: Program Variant-Guided Test Augmentation for Repository-Level Repair Benchmarks

基准测试足够强大吗？基于突变的诊断和回归套件的增强

Chenglin Li, Yisen Xu, Zehao Wang, Shin Hwei Tan, Tse-Hsun, Chen

专题命中 医学数据与评测 ：diagnosis(title)

AI总结 本文提出STING框架，通过语义改变的程序变体增强回归测试，提高基准测试的可靠性。实验显示，77%的实例存在至少一个存活变体，测试覆盖率提升，修复率下降，揭示了基准测试的不足。

Comments Accepted at the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026)

Clinician-Level Agreement Without Clinical Caution: LLM Evaluator Limits in Medical AI Benchmarking

临床医生级别的一致性缺乏临床谨慎：LLM评估者在医学AI基准测试中的局限性

William Philipp, Finn Fassbender, Daniel Fister, Thorsten Langer, Martje G. Pauly, Rebecca Herzog, Markus A. Hobert, Theresa Paulus, Alexander Baumann, Chi Wang Ip, Lukas L. Goede, Johanna Reimer, Sebastian Löns, Ronald Böck, Sebastian Fudickar

机构 * University of Luebeck（吕贝克大学） ； University of Tübingen（图宾根大学） ； University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院） ； University Hospital Würzburg（维尔茨堡大学医院） ； Charité – Universitätsmedizin Berlin（柏林夏里特医学院） ； Genie Enterprise Deutschland GmbH 纠错

专题命中 医学数据与评测 ：medical AI(title)

AI总结 针对德语开放回答临床基准MedQADE，评估LLM评估者与医生的对齐程度，发现统计一致性高但缺乏临床元认知，且存在系统性的模型谱系偏差。

Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis

探测、融合与可信度：基础模型表示在多模态癌症分析中的系统评估

Jingyu Hu, Giuseppe Tripodi, Reed Naidoo, Sarah F. McGough, Tapabrata Chakraborti

机构 * The Alan Turing Institute（艾伦·图灵研究所） ； University of Bristol（布里斯托大学） ； University of Manchester（曼彻斯特大学） ； The Institute of Cancer Research（癌症研究所） ； Genentech（基因泰克） 纠错

专题命中 医学数据与评测 ：pathology(abstract)；diagnosis(abstract)；分类 cs.LG、q-bio

AI总结 系统评估基础模型表示在计算病理学任务中的性能，发现图像和组学表示互补，多模态融合在单模态不占优时有效，并利用共形预测验证了不确定性感知推理的临床价值。

Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System

以部署为中心的评估：预测临床大语言模型系统中的查询级拒绝风险

Alyssa Unell, Miguel Fuentes, Brenna Li, Bridget Lin, Meena Jagadeesan, Sanmi Koyejo, Nigam Shah

机构 * Department of Computer Science（计算机科学系） ； Department of Medicine（医学系） ； Department of Biomedical Data Science（生物医学数据科学系） 纠错

专题命中 医学数据与评测 ：clinical LLM(title)

AI总结 针对临床大语言模型系统，提出基于部署上下文（如提供者类型、科室名称）的预响应分类器，预测用户拒绝风险，AUROC达0.719，并展示其在触发护栏和弃权中的效用。

Evaluating Factual Density in Multi-Source RAG: A Study in Medical AI Accuracy

评估多源RAG中的事实密度：医学AI准确性研究

Michael R. DeMarco

机构 * NexusAgentics 纠错

专题命中 医学数据与评测 ：medical AI(title)

AI总结 针对标准RAG管道因专家盲视效应而忽视高密度事实证据的问题，提出事实密度（FD*）作为检索优化信号，通过概率事实性分析预处理和Z-score归一化消除长度偏差，在HealthFC基准上实现100%系统综述覆盖率。

Comments 16 pages, 8 tables. Includes Experiment 3 results (n=11, Wilcoxon p=0.0619). Preliminary findings; powered Experiment 3 and Graph RAG extension identified as future work. Updated from v1

Incentives, Equilibria, and the Limits of Healthcare AI: A Game-Theoretic Perspective

激励、均衡与医疗AI的局限：博弈论视角

Ari Ercole

机构 * Cambridge Centre for AI in Medicine, University of Cambridge, UK（剑桥大学医学人工智能中心） ； Magdalene College, University of Cambridge, UK（剑桥大学玛格丽特学院） 纠错

专题命中 医学数据与评测 ：healthcare AI(title)

AI总结 本文通过住院容量管理的协调问题，描述三种AI部署形式，并分析其对系统行为的影响，指出只有改变激励结构的干预才能改变稳定均衡，为医疗AI的采购、治理和评估提供实践启示。

Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI

讲述推测性故事以帮助人类想象医疗AI的害处

Xingmeng Zhao, Tongnian Wang, Dan Schumacher, Veronica Rammouz, Anthony Rios

机构 * The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校） 纠错

专题命中 医学数据与评测 ：healthcare AI(title)

AI总结 本文提出一种以人类为中心的框架，通过生成用户故事和多代理讨论，帮助人们在部署前创造性地思考医疗AI的潜在利弊。实验表明，阅读故事的参与者能更全面地识别各种害处。

Comments 8 pages main + Appendix; Accepted to ACL Findings 2026