医学影像分析新范式：用海量无标注数据训练通用模型
Foundation Models in Medical Imaging: A Review and Outlook
- 通过自监督学习从大量无标签医学图像中提取通用特征
- 在病理/放射/眼科三领域验证，可低资源适配临床任务
- 适合关注AI医疗落地的医生、工程师与研究者
基础模型（Foundation Models, FMs）正改变医学影像分析方式，通过在大规模未标注数据上预训练，学习通用视觉特征，无需依赖大量人工标注即可适应特定临床任务。本文综述了超过150项研究，系统分析了病理学、放射学和眼科学中FMs的发展与应用，涵盖模型架构、自监督学习方法及下游适应策略等核心组件。同时对比不同领域的设计选择，揭示共性与差异，并讨论当前面临的关键挑战与开放问题，为未来研究提供指引。
原文摘要 · Abstract (English)
Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FMs are pre-trained to learn general-purpose visual features that can later be adapted to specific clinical tasks with little additional supervision. In this review, we examine how FMs are being developed and applied in pathology, radiology, and ophthalmology, drawing on evidence from over 150 studies. We explain the core components of FM pipelines, including model architectures, self-supervised learning methods, and strategies for downstream adaptation. We also review how FMs are being used in each imaging domain and compare design choices across applications. Finally, we discuss key challenges and open questions to guide future research.
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