arXivDaily arXiv每日学术速递 周一至周五更新

科学与医疗

医学 AI

医学智能、临床 AI、医学影像、病理、诊断和医疗健康大模型。

2026-02-03 至 2026-02-03 共收录 5 信号源:cs.CV, cs.LG, q-bio, eess.IV, eess.SP

1. 医学数据与评测 5 篇

2602.01633 2026-02-03 cs.CV 79%

Federated Vision Transformer with Adaptive Focal Loss for Medical Image Classification

联邦视觉Transformer与自适应焦点损失用于医学图像分类

Xinyuan Zhao, Yihang Wu, Ahmad Chaddad, Tareef Daqqaq, Reem Kateb

机构 * School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China, 541004(桂林电子科技大学人工智能学院) The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Montreal, Canada, H3C 1K3(影像、视觉与人工智能实验室) College of Medicine, Taibah University, Al Madinah, Saudi Arabia, 42353(塔布大学医学院) Department of Radiology, Prince Mohammed Bin Abdulaziz Hospital, Ministry of National Guard Health Affairs, Al Madinah, Saudi Arabia, 42324(王子穆罕默德·本·阿卜杜勒阿齐兹医院放射科) College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia, 42353(塔布大学计算机科学与工程学院) Engineering, Jeddah University, Jeddah, Saudi Arabia, 23445(吉达大学工程学院)

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

AI总结 本研究提出一种结合动态自适应焦点损失和客户端感知聚合策略的联邦学习框架,提升医学图像分类的准确率。

Comments Accepted in Knowledge-Based Systems

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2602.00249 2026-02-03 cs.CV cs.AI 74%

SANEval: Open-Vocabulary Compositional Benchmarks with Failure-mode Diagnosis

SANEval: 开放词汇组合评估基准与失败模式诊断

Rishav Pramanik, Ian E. Nielsen, Jeff Smith, Saurav Pandit, Ravi P. Ramachandran, Zhaozheng Yin

机构 * Stony Brook University(石溪大学) nd Set AI Corp.(第二集AI公司) Rowan University(罗文大学)

专题命中 医学数据与评测 :diagnosis(title);分类 cs.CV

AI总结 SANEval提出一种开放词汇组合评估基准,结合大型语言模型和增强对象检测器,通过实验展示其在属性绑定、空间关系和数值任务上的评估效果优于现有基准。

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2602.01779 2026-02-03 cs.AI 67%

LingLanMiDian: Systematic Evaluation of LLMs on TCM Knowledge and Clinical Reasoning

LingLanMiDian: LLMs在中医知识与临床推理上的系统评估

Rui Hua, Yu Wei, Zixin Shu, Kai Chang, Dengying Yan, Jianan Xia, Zeyu Liu, Hui Zhu, Shujie Song, Mingzhong Xiao, Xiaodong Li, Dongmei Jia, Zhuye Gao, Yanyan Meng, Naixuan Zhao, Yu Fu, Haibin Yu, Benman Yu, Yuanyuan Chen, Fei Dong, Zhizhou Meng, Pengcheng Yang, Songxue Zhao, Lijuan Pei, Yunhui Hu, Kan Ding, Jiayuan Duan, Wenmao Yin, Yang Gu, Runshun Zhang, Qiang Zhu, Jian Yu, Jiansheng Li, Baoyan Liu, Wenjia Wang, Xuezhong Zhou

机构 * Department of Computer Science and Technology, Beijing Jiaotong University, Beijing, China(北京交通大学计算机科学与技术学院) Tianjin Tasly Digital Chinese Medicine Technology Co., Ltd.(天津塔斯丽数字中医科技有限公司) Tasly Biopharmaceuticals Co., Ltd.(塔斯丽生物医药有限公司) State Key Laboratory of Chinese Medicine Modernization, Tianjin, China(中药现代化国家工程实验室,天津,中国) Institute of Liver Diseases, Hubei Key Laboratory of the theory and application research of liver and kidney in traditional Chinese medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China(肝病研究所,湖北省中医肝肾理论与应用研究重点实验室,湖北省中医药研究院,武汉,中国) Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, China(湖北中医药大学附属医院,武汉,中国) Hubei Province Academy of Traditional Chinese Medicine, Wuhan, China(湖北省中医药研究院,武汉,中国) Department of Gastroenterology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China(消化内科,广安门医院,中国中医科学院,北京,中国) China Academy of Chinese Medical Sciences, Beijing, China(中国中医科学院,北京,中国) China Institute for History of Medicine and Medical Literature, China Academy of Chinese Medical Sciences, Beijing, China(中国中医科学院中国医学史与医学文献研究所,北京,中国) Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China(北京中医研究院,北京中医药大学,北京,中国) Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing 100029, China(北京中医药大学第三附属医院,北京中医药大学,北京100029,中国)

专题命中 医学数据与评测 :medical AI(abstract);diagnosis(abstract)

AI总结 LingLan基准测试系统评估了LLMs在中医知识和临床推理上的表现,揭示了当前模型与专家在专门推理上的差距。

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2602.00102 2026-02-03 eess.IV cs.AI cs.LG 62%

Radiomics in Medical Imaging: Methods, Applications, and Challenges

医学影像中的放射组学:方法、应用与挑战

Fnu Neha, Deepak kumar Shukla

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

AI总结 本文系统分析了放射组学的全流程,探讨了特征稳定性、模型可靠性及临床转化有效性,并提出了标准化、领域转移和临床部署等未来研究方向。

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2602.01020 2026-02-03 cs.CV 57%

Effectiveness of Automatically Curated Dataset in Thyroid Nodules Classification Algorithms Using Deep Learning

深度学习在甲状腺结节分类算法中自动整理数据集的有效性

Jichen Yang, Jikai Zhang, Benjamin Wildman-Tobriner, Maciej A. Mazurowski

机构 * Department of Electrical & Computer Engineering, Duke University(电气与计算机工程系,杜克大学) Department of Radiology, Duke University Medical Center(放射科,杜克大学医学中心)

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

AI总结 本研究探讨了自动整理数据集在深度学习甲状腺结节分类算法中的有效性,发现其能显著提升模型性能。

Comments 9 pages, 3 figures

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