  In digital pathology, different staining procedures and scanners cause
substantial color variations in whole-slide images (WSIs), especially across
different laboratories. These color shifts result in a poor generalization of
deep learning-based methods from the training domain to external pathology
data. To increase test performance, stain normalization techniques are used to
reduce the variance between training and test domain. Alternatively, color
augmentation can be applied during training leading to a more robust model
without the extra step of color normalization at test time. We propose a novel
color augmentation technique, HistAuGAN, that can simulate a wide variety of
realistic histology stain colors, thus making neural networks stain-invariant
when applied during training. Based on a generative adversarial network (GAN)
for image-to-image translation, our model disentangles the content of the
image, i.e., the morphological tissue structure, from the stain color
attributes. It can be trained on multiple domains and, therefore, learns to
cover different stain colors as well as other domain-specific variations
introduced in the slide preparation and imaging process. We demonstrate that
HistAuGAN outperforms conventional color augmentation techniques on a
classification task on the publicly available dataset Camelyon17 and show that
it is able to mitigate present batch effects.
