Fig. 2

Quantitative histopathology analysis identified tumor grade. a ROC curves of convolutional neural networks that classified the pathology grade of serous ovarian carcinoma. The sensitivity and specificity for identifying high-grade serous ovarian carcinoma are shown. AUC in the independent test set: AlexNet = 0.760 ± 0.082; GoogLeNet = 0.810 ± 0.067; VGGNet = 0.812 ± 0.088. b The gradient-weighted class activation map (grad-CAM) of a histopathology image of a low-grade ovarian cancer patient and the original hematoxylin-and-eosin-stained histopathology image. Tumor cells and differentiated cellular structures received higher weighted in the grad-CAM. c The grad-CAM of a histopathology image of a high-grade ovarian cancer patient and the original hematoxylin-and-eosin-stained histopathology image. Clusters of tumor cells with poor differentiation were highlighted by the grad-CAM