Extended Data Fig. 2: MIL model classification performance for different cancer datasets.
From: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Performance on the respective test datasets was measured in terms of AUC. a, Best results were achieved on the prostate dataset (n = 1,784), with an AUC of 0.989 at 20× magnification. b, For BCC (n = 1,575), the model trained at 5× performed the best, with an AUC of 0.990. c, The worst performance came on the breast metastasis detection task (n = 1,473), with an AUC of 0.965 at 20×. The axillary lymph node dataset is the smallest of the three datasets, which is in agreement with the hypothesis that larger datasets are necessary to achieve lower error rates on real-world clinical data.
