Fig. 1.

The overview of the training process. WSIs were divided into test and training sets. In our study, 619 patients had more than one WSIs, which were excluded from the test set and only used for training. Tissue patch samples of size 1000 × 1000 pixels derived from WSIs were fed into deep networks (KimiaNet and DenseNet) for feature extraction. The output of the last pooling layer was used as deep features. The extracted deep features were later used to train a smaller network with two hidden layers to identify tissue sources