集成技术2025，Vol.14Issue(2)：24-32,9.DOI:10.12146/j.issn.2095-3135.20241111001
多实例学习在医学图像分析中的应用进展
Application Progress of Multi-instance Learning in Medical Image Analysis
摘要
Abstract
Multiple-instance learning(MIL),as a weakly supervised learning method,has been widely applied in the field of medical image analysis in recent years.The paper reviews the progress of MIL applications in whole slide images,with a detailed analysis of its roles in tumor detection,subtype classification,and survival prediction.MIL holds unique advantages in weakly supervised learning,which can be optimized and extended through the introduction of new mechanisms to adapt to a broader range of application scenarios.The paper first reviews some widely used or uniquely advantageous MIL models,elaborating on their technical features and specific application contexts.Secondly,it introduces the application and technology advancements of MIL in multimodal medical image analysis.Finally,the current research progress of MIL is summarized,and its future development prospects are explored.关键词
图像分析/多实例学习/医学图像/机器学习/深度学习Key words
image analysis/multiple-instance learning/medical images/machine learning/deep learning分类
基础医学引用本文复制引用
谢卓恒,伊鸣,黄新瑞..多实例学习在医学图像分析中的应用进展[J].集成技术,2025,14(2):24-32,9.基金项目
北京大学医学部教育教学研究课题项目(2022YB17) （2022YB17）
北京市自然科学基金面上项目(4242004) （4242004）
国家蛋白质科学研究(北京)设施北京大学分中心开放课题项目(KF-202402) （北京）
国家自然科学基金项目(32271053) （32271053）
北京市自然科学基金-海淀原始创新联合基金项目(L222016) This work is supported by Peking University Health Science Center Medical Education Research Funding Project(2022YB17),Beijing Natural Science Foundation(4242004),Open Research Fund of the National Center for Protein Sciences at Peking University in Beijing(KF-202402),National Natural Science Foundation of China(32271053),and Beijing Natural Science Foundation-Haidian Original Innovation Joint Fund(L222016) （L222016）
