通过动态特征学习解锁自适应数字病理学
Unlocking adaptive digital pathology through dynamic feature learning
AI总结：
本文提出PathFiT动态特征学习方法，可即插即用于病理基础模型以增强适应性，并在涵盖35个任务的20TB数字病理基准上取得34项最优性能。
摘要：
基础模型已经彻底改变了数字病理学的范式，因为它们利用通用特征来模拟真实世界的病理实践，从而能够对关键组织学模式进行定量分析，并剖析癌症特异性信号。然而，这些静态的通用特征限制了在不断演变的临床应用需求中的灵活性和病理相关性，阻碍了当前模型的广泛使用。在此，我们介绍PathFiT，一种动态特征学习方法，可以轻松地插入到各种病理学基础模型中，以解锁它们的适应性。同时，PathFiT能够在不同的病理学应用中实现无缝实施，无论下游特异性如何。为了验证PathFiT，我们构建了一个数字病理学基准，包含超过20TB的互联网和真实世界数据，涵盖28个H&E染色任务和7个专门成像任务，包括Masson三色染色和免疫荧光图像。通过将PathFiT应用于代表性的病理学基础模型，我们在35个任务中的34个上展示了最先进的性能，在23个任务上取得了显著改进，并在专门成像任务上超越了10.20%。PathFiT的卓越性能和多功能性为计算病理学开辟了新的途径。
英文摘要：
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.
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。
