Extended Data Fig. 2: Average AUROCs sorted by cancer type and on scarce data tasks.
From: Benchmarking foundation models as feature extractors for weakly supervised computational pathology
Average AUROC scores of the five folds of each foundation model. Taskwise normalization for better comparison of the foundation models. Tasks are sorted by their mean AUROC across all models, while models are sorted by their mean AUROC across all tasks. A, The 31 tasks were grouped by cancer type (5 tasks for NSCLC, 5 tasks for BRCA, 8 tasks for STAD, 13 tasks for CRC). Models are sorted by average performance. B, Only tasks with rare positive cases (>15%) in the TCGA training cohort are shown. To avoid cancer type imbalance, these tasks are only evaluated in DACHS, Kiel and CPTAC LUAD.
