系统梳理病理基础模型进展，为癌症诊断提供分析框架。
A Survey of Pathology Foundation Model: Progress and Future Directions
- 按模型范围、预训练、设计构建分层分类体系
- 涵盖滑片级、切片级等四类评估任务，建立全面基准
- 指出模型开发与应用中的关键挑战，指引未来方向
计算病理学依赖多实例学习分析全切片图像以实现自动化癌症诊断，性能高度依赖特征提取器与聚合器。近期基于大规模组织病理数据预训练的病理基础模型（PFM）显著提升了二者能力，但缺乏系统的分析框架。本文提出一种自上而下的分层分类法，涵盖模型范围、预训练方式与模型设计三维度，适用于任何领域基础模型分析。同时，系统划分PFM评估任务为滑片级、切片级、多模态及生物任务，并提供完整基准标准。分析揭示了模型研发中的核心挑战：病理特异性方法、端到端预训练、数据-模型可扩展性；以及使用阶段的问题：有效适配与模型维护，为该领域的未来发展指明方向。相关资源见 https://github.com/BearCleverProud/AwesomeWSI。
原文摘要 · Abstract (English)
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.
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