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Based on digital pathology slice scanning technology, artificial intelligencealgorithms represented by deep learning have achieved remarkable results in thefield of computational pathology. Compared to other medical images, pathologyimages are more difficult to annotate, and thus, there is an extreme lack ofavailable datasets for conducting supervised learning to train robust deeplearning models. In this paper, we propose a self-supervised learning (SSL)model, the global contrast-masked autoencoder (GCMAE), which can train theencoder to have the ability to represent local-global features of pathologicalimages, also significantly improve the performance of transfer learning acrossdata sets. In this study, the ability of the GCMAE to learn migratablerepresentations was demonstrated through extensive experiments using a total ofthree different disease-specific hematoxylin and eosin (HE)-stained pathologydatasets: Camelyon16, NCTCRC and BreakHis. In addition, this study designed aneffective automated pathology diagnosis process based on the GCMAE for clinicalapplications. The source code of this paper is publicly available athttps://github.com/StarUniversus/gcmae.

Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners