  Color inconsistency is an inevitable challenge in computational pathology,
which generally happens because of stain intensity variations or sections
scanned by different scanners. It harms the pathological image analysis
methods, especially the learning-based models. A series of approaches have been
proposed for stain normalization. However, most of them are lack flexibility in
practice. In this paper, we formulated stain normalization as a digital
re-staining process and proposed a self-supervised learning model, which is
called RestainNet. Our network is regarded as a digital restainer which learns
how to re-stain an unstained (grayscale) image. Two digital stains, Hematoxylin
(H) and Eosin (E) were extracted from the original image by Beer-Lambert's Law.
We proposed a staining loss to maintain the correctness of stain intensity
during the restaining process. Thanks to the self-supervised nature, paired
training samples are no longer necessary, which demonstrates great flexibility
in practical usage. Our RestainNet outperforms existing approaches and achieves
state-of-the-art performance with regard to color correctness and structure
preservation. We further conducted experiments on the segmentation and
classification tasks and the proposed RestainNet achieved outstanding
performance compared with SOTA methods. The self-supervised design allows the
network to learn any staining style with no extra effort.
