病理基础模型综述：进展与未来方向
A Survey of Pathology Foundation Model: Progress and Future Directions
AI总结：
该综述针对病理基础模型缺乏系统分析框架的问题，提出适用于各领域基础模型的分层分类体系，梳理PFM评估任务类型与基准标准，明确开发与应用的关键挑战并指明未来方向，同时汇总相关资源。
摘要：
计算病理学通过分析全切片图像实现自动化癌症诊断，其核心依赖多示例学习，而该方法的性能很大程度上取决于特征提取器和聚合器。近期，在大规模组织病理学数据上预训练的病理基础模型（Pathology Foundation Models, PFMs）显著优化了提取器与聚合器，但目前缺乏系统的分析框架。本综述提出一种分层分类体系，以自上而下的思路对PFMs进行梳理，该思路适用于任意领域的基础模型分析，涵盖三个维度：模型范围、模型预训练、模型设计。此外，本文系统地将PFM评估任务划分为切片级、斑块级、多模态和生物学任务四类，并给出了全面的基准测试标准。通过分析，本文明确了PFM开发（病理专属方法论、端到端预训练、数据-模型可扩展性）与应用（高效适配、模型维护）层面的关键挑战，为这一前景广阔的领域指明了未来方向。本综述提及的资源可在https://github.com/BearCleverProud/AwesomeWSI获取。
英文摘要：
Computational pathology, which involves analyzing whole slide images for automated cancer diagnosis, relies on multiple instance learning, where performance depends heavily on the feature extractor and aggregator. Recent Pathology Foundation Models (PFMs), pretrained on large-scale histopathology data, have significantly enhanced both the extractor and aggregator, but they lack a systematic analysis framework. In this survey, we present a hierarchical taxonomy organizing PFMs through a top-down philosophy applicable to foundation model analysis in any domain: model scope, model pretraining, and model design. Additionally, we systematically categorize PFM evaluation tasks into slide-level, patch-level, multimodal, and biological tasks, providing comprehensive benchmarking criteria. Our analysis identifies critical challenges in both PFM development (pathology-specific methodology, end-to-end pretraining, data-model scalability) and utilization (effective adaptation, model maintenance), paving the way for future directions in this promising field. Resources referenced in this survey are available at https://github.com/BearCleverProud/AwesomeWSI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
