A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models
This work addresses the need for standardized evaluation of pathology foundation models to bridge research and clinical deployment, though it is incremental as it focuses on benchmarking existing models.
The authors tackled the problem of comparing public self-supervised pathology foundation models by establishing a benchmark using clinical datasets from two medical centers, resulting in systematic performance assessments and insights for training and selecting models.
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.