  Domain shift in digital histopathology can occur when different stains or
scanners are used, during stain translation, etc. A deep neural network trained
on source data may not generalise well to data that has undergone some domain
shift. An important step towards being robust to domain shift is the ability to
detect and measure it. This article demonstrates that the PixelCNN and domain
shift metric can be used to detect and quantify domain shift in digital
histopathology, and they demonstrate a strong correlation with generalisation
performance. These findings pave the way for a mechanism to infer the average
performance of a model (trained on source data) on unseen and unlabelled target
data.
