  Based on digital pathology slice scanning technology, artificial intelligence
algorithms represented by deep learning have achieved remarkable results in the
field of computational pathology. Compared to other medical images, pathology
images are more difficult to annotate, and thus, there is an extreme lack of
available datasets for conducting supervised learning to train robust deep
learning models. In this paper, we propose a self-supervised learning (SSL)
model, the global contrast-masked autoencoder (GCMAE), which can train the
encoder to have the ability to represent local-global features of pathological
images, also significantly improve the performance of transfer learning across
data sets. In this study, the ability of the GCMAE to learn migratable
representations was demonstrated through extensive experiments using a total of
three different disease-specific hematoxylin and eosin (HE)-stained pathology
datasets: Camelyon16, NCTCRC and BreakHis. In addition, this study designed an
effective automated pathology diagnosis process based on the GCMAE for clinical
applications. The source code of this paper is publicly available at
https://github.com/StarUniversus/gcmae.
