  Computational pathology is a domain that aims to develop algorithms to
automatically analyze large digitized histopathology images, called whole slide
images (WSI). WSIs are produced scanning thin tissue samples that are stained
to make specific structures visible. They show stain colour heterogeneity due
to different preparation and scanning settings applied across medical centers.
Stain colour heterogeneity is a problem to train convolutional neural networks
(CNN), the state-of-the-art algorithms for most computational pathology tasks,
since CNNs usually underperform when tested on images including different stain
variations than those within data used to train the CNN. Despite several
methods that were developed, stain colour heterogeneity is still an unsolved
challenge that limits the development of CNNs that can generalize on data from
several medical centers. This paper aims to present a novel method to train
CNNs that better generalize on data including several colour variations. The
method, called H&E-adversarial CNN, exploits H&E matrix information to learn
stain-invariant features during the training. The method is evaluated on the
classification of colon and prostate histopathology images, involving eleven
heterogeneous datasets, and compared with five other techniques used to handle
stain colour heterogeneity. H&E-adversarial CNNs show an improvement in
performance compared to the other algorithms, demonstrating that it can help to
better deal with stain colour heterogeneous images.
