A clinical benchmark of public self-supervised pathology foundation models

The use of self-supervised learning 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…

• MS
  Memorial Sloan-Kettering Cancer Center

  Award: P30CA008748
• GC
  Georgia Clinical and Translational Science Alliance

  Award: UL1TR004419
• NI
  National Institutes of Health

  Awards: UL1TR004419, P30CA008748
• NC
  National Cancer Institute

  Award: P30CA008748
• UN
  U.S. National Library of Medicine

  Award: LM013766