  An important part of Digital Pathology is the analysis of multiple digitised
whole slide images from differently stained tissue sections. It is common
practice to mount consecutive sections containing corresponding microscopic
structures on glass slides, and to stain them differently to highlight specific
tissue components. These multiple staining modalities result in very different
images but include a significant amount of consistent image information. Deep
learning approaches have recently been proposed to analyse these images in
order to automatically identify objects of interest for pathologists. These
supervised approaches require a vast amount of annotations, which are difficult
and expensive to acquire---a problem that is multiplied with multiple
stainings. This article presents several training strategies that make progress
towards stain invariant networks. By training the network on one commonly used
staining modality and applying it to images that include corresponding but
differently stained tissue structures, the presented unsupervised strategies
demonstrate significant improvements over standard training strategies.
