Figure 1. Processing pipeline of a convolutional neural network for the detection of prostate cancer in H&E-stained whole slide biopsy specimens.

The four layers indicated with C, meaning a convolutional layer, can be considered a ‘feature extraction’-stage were consecutively higher level features are extracted from the image patch. The layers indicated by the letter M are max pooling layers which reduce image size and provide improved translational invariance to the network. The last three layers are the ‘classification’ layers (indicated with F) which, based on the given features, indicates whether the image patch contains cancer or not. Such a network can subsequently be applied to every pixel in a whole slide image in a ‘sliding window’-fashion²⁷.