Fig. 3: SL models are data efficient.
From: Swarm learning for decentralized artificial intelligence in cancer histopathology
a, Classification performance (AUROC) for prediction of BRAF mutational status at the patient level in the QUASAR cohort. Total cohort sizes (number of patients, for BRAF mutational status) in the training sets are 642 for Epi700, 2,075 for DACHS and 500 for TCGA. Total cohort size (number of patients, for BRAF mutational status) in the test set is 1,477 for QUASAR. b, Classification performance (AUROC) for prediction of MSI/dMMR status at the patient level in the QUASAR cohort. Total cohort sizes (number of patients, for MSI/dMMR) in the training sets are 594 for Epi700, 2,039 for DACHS and 426 for TCGA. Total cohort size (number of patients, for MSI/dMMR status) in the test set is 1,774 for QUASAR. c, Classification performance (AUROC) for prediction of MSI/dMMR status at the patient level in the YCR BCIP cohort. Total cohort sizes (number of patients, for MSI/dMMR status) in the training sets are identical to those in b. Total cohort size (number of patients, for MSI/dMMR status) in the test set is 805 for YCR BCIP. In a–c, the boxes show the median values and quartiles, the whiskers show the rest of the distribution (except for points identified as outliers), and all original data points are shown.
