  The application of supervised deep learning methods in digital pathology is
limited due to their sensitivity to domain shift. Digital Pathology is an area
prone to high variability due to many sources, including the common practice of
evaluating several consecutive tissue sections stained with different staining
protocols. Obtaining labels for each stain is very expensive and time consuming
as it requires a high level of domain knowledge. In this article, we propose an
unsupervised augmentation approach based on adversarial image-to-image
translation, which facilitates the training of stain invariant supervised
convolutional neural 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 method demonstrates
significant improvements over other approaches. These benefits are illustrated
in the problem of glomeruli segmentation in seven different staining modalities
(PAS, Jones H&E, CD68, Sirius Red, CD34, H&E and CD3) and analysis of the
learned representations demonstrate their stain invariance.
