State of machine and deep learning in histopathological applications in digestive diseases

Figure 3 General deep learning training and prediction approaches. A: Examples pipelines for fully supervised, weakly supervised, and unsupervised learning methods for training patch classifiers are shown; B: Two pipelines translating patch-level information into whole-slide image-level predictions are shown. The top approach utilizes a patch classifier trained by one of the approaches in (A). The bottom approach uses a convolutional neural network feature extractor to generate patch representations that are fed into a long short-term memory or recurrent neural network. H&E: Hematoxylin and eosin; WSI: Whole-slide image; CNN: Convolutional neural network; RNN: Recurrent neural network; LSTM: Long short-term memory; CAE: Convolutional autoencoder.