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.
@article{arxiv.1810.10338,
title = {Strategies for Training Stain Invariant CNNs},
author = {Thomas Lampert and Odyssée Merveille and Jessica Schmitz and Germain Forestier and Friedrich Feuerhake and Cédric Wemmert},
journal= {arXiv preprint arXiv:1810.10338},
year = {2018}
}