  Domain shift is a significant problem in histopathology. There can be large
differences in data characteristics of whole-slide images between medical
centers and scanners, making generalization of deep learning to unseen data
difficult. To gain a better understanding of the problem, we present a study on
convolutional neural networks trained for tumor classification of H&E stained
whole-slide images. We analyze how augmentation and normalization strategies
affect performance and learned representations, and what features a trained
model respond to. Most centrally, we present a novel measure for evaluating the
distance between domains in the context of the learned representation of a
particular model. This measure can reveal how sensitive a model is to domain
variations, and can be used to detect new data that a model will have problems
generalizing to. The results show how learning is heavily influenced by the
preparation of training data, and that the latent representation used to do
classification is sensitive to changes in data distribution, especially when
training without augmentation or normalization.
