Extended Data Fig. 2: Training principles of the model.
From: High-accuracy prostate cancer pathology using deep learning
Three classes were used for training (benign glandular, benign non-glandular, tumour) with stain normalization. NASNetLarge architecture was used with addition of three new layers for classification (flatten, fully connected, and classification layers). Probability of being benign could be summarized from output probabilities of two benign classes. Training principles and parameters are presented in boxes.
