The use of self-supervised learning (SSL) to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantities of clinical data have been made publicly available in recent months. This will significantly enhance scientific research in computational pathology and help bridge the gap between research and clinical deployment. With the increase in availability of public foundation models of different sizes, trained using different algorithms on different datasets, it becomes important to establish a benchmark to compare the performance of such models on a variety of clinically relevant tasks spanning multiple organs and diseases. In this work, we present a collection of pathology datasets comprising clinical slides associated with clinically relevant endpoints including cancer diagnoses and a variety of biomarkers generated during standard hospital operation from two medical centers. We leverage these datasets to systematically assess the performance of public pathology foundation models and provide insights into best practices for training new foundation models and selecting appropriate pretrained models.

利用自我监督学习(SSC)训练病理基础模型的应用在过去几年有了显著增长，公开提供了多个在大量临床数据上训练的模型，将极大地增强计算病理学的科研能力、弥合研究与临床应用的鸿沟。本研究提供了一个包含具有临床相关终点的病理学数据集合，其中包括与癌症诊断以及来自两个医疗中心标准医院运营期间生成的各种生物标记物相关的临床切片，利用这些数据集对公开的病理基础模型的性能进行系统评估，并提供培训新的基础模型和选择适当的预训练模型的最佳实践见解。

公共自我监督病理基础模型的临床基准测试