让病理模型动态学习特征，提升临床适应性
Unlocking adaptive digital pathology through dynamic feature learning
- 引入PathFiT动态特征学习方法，增强模型灵活性
- 34/35任务达顶尖水平，专病成像任务提升10.20%
- 适配多种病理任务，无需调整即可部署
基础模型已革新数字病理学范式，利用通用特征模拟真实病理实践，实现关键组织学模式的定量分析和癌症特异性信号解构。然而，静态通用特征限制了模型在不断演化的临床需求中的灵活性与病理相关性，制约了现有模型的广泛应用。本文提出PathFiT，一种可无缝嵌入各类病理基础模型的动态特征学习方法，显著提升其适应能力。为验证效果，构建包含超20TB互联网与真实世界数据的数字病理基准，涵盖28项H&E染色任务和7项特殊成像任务（如Masson's Trichrome染色、免疫荧光图像）。将PathFiT应用于代表性病理基础模型，在35项任务中实现34项领先表现，23项任务有显著提升，特殊成像任务性能超越原有方法10.20%。PathFiT的卓越性能与通用性为计算病理学开辟新路径。
原文摘要 · Abstract (English)
Foundation models have revolutionized the paradigm of digital pathology, as they leverage general-purpose features to emulate real-world pathological practices, enabling the quantitative analysis of critical histological patterns and the dissection of cancer-specific signals. However, these static general features constrain the flexibility and pathological relevance in the ever-evolving needs of clinical applications, hindering the broad use of the current models. Here we introduce PathFiT, a dynamic feature learning method that can be effortlessly plugged into various pathology foundation models to unlock their adaptability. Meanwhile, PathFiT performs seamless implementation across diverse pathology applications regardless of downstream specificity. To validate PathFiT, we construct a digital pathology benchmark with over 20 terabytes of Internet and real-world data comprising 28 H\&E-stained tasks and 7 specialized imaging tasks including Masson's Trichrome staining and immunofluorescence images. By applying PathFiT to the representative pathology foundation models, we demonstrate state-of-the-art performance on 34 out of 35 tasks, with significant improvements on 23 tasks and outperforming by 10.20% on specialized imaging tasks. The superior performance and versatility of PathFiT open up new avenues in computational pathology.
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