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.

翻译：自监督学习（SSL）在病理学基础模型训练中的应用在过去几年显著增加。值得注意的是，近期已有多个基于大量临床数据训练的模型被公开发布。这将极大促进计算病理学的科学研究，并有助于弥合研究与临床部署之间的差距。随着不同规模、采用不同算法在不同数据集上训练的公共基础模型日益增多，建立一个基准来评估这些模型在跨越多个器官与疾病的各类临床相关任务上的性能变得至关重要。本研究提出了一套病理学数据集集合，包含来自两家医疗中心的临床切片数据，这些切片关联着包括癌症诊断在内的临床相关终点指标，以及标准医院运营中产生的多种生物标志物。我们利用这些数据集系统评估了公共病理学基础模型的性能，并为训练新基础模型及选择合适预训练模型的最佳实践提供了见解。