Fig. 1

Integrative histopathology-functional omics analyses on serous ovarian carcinoma. a A model of the informatics workflow in this study. b Convolutional neural networks identified regions with tumor cells of serous ovarian carcinoma. Receiver operating characteristic (ROC) curves of convolutional neural networks that classified regions with tumor cells from those without tumor cells in the independent test set are shown. Areas under the receiver operating characteristic curves (AUCs) in the independent test set: AlexNet = 0.955 ± 0.010; GoogLeNet = 0.974 ± 0.004; VGGNet = 0.975 ± 0.001. c Gradient-weighted class activation maps (grad-CAMs) confirmed that the CNN models focused on the cancerous part of the histopathology slides when classifying malignant tissues from benign ones. The original hematoxylin-and-eosin-stained histopathology image was also shown