Benchmarking foundation models as feature extractors for weakly-supervised computational pathology

Understand

Advancements in artificial intelligence have driven the development of numerous pathology foundation models capable of extracting clinically relevant information.

• However, there is currently limited literature independently evaluating these foundation models on truly external cohorts and clinically-relevant tasks to uncover adjustments for future improvements.
• In this study, we benchmarked 19 histopathology foundation models on 13 patient cohorts with 6,818 patients and 9,528 slides from lung, colorectal, gastric, and breast cancers.
• The models were evaluated on weakly-supervised tasks related to biomarkers, morphological properties, and prognostic outcomes.