Self-supervised learning (SSL) is increasingly used to train pathology foundation models. Here, the authors introduce a pathology benchmark set generated during standard clinical workflows that includes multiple cancer and disease types; then leverage it to assess the performance of multiple public SSL pathology foundation models and to provide best practices for model training and selection.
- Gabriele Campanella
- Shengjia Chen
- Chad Vanderbilt
