Global contrast-masked autoencoders are powerful pathological representation learners

Hao Quan, Xingyu Li, Weixing Chen, Qun Bai, Mingchen Zou, Ruijie Yang, Tingting Zheng, Ruiqun Qi, Xinghua Gao, Xiaoyu Cui

Abstract: Highlight•We have designed two self-supervised pretext tasks: masking image reconstruction and contrastive learning, which can train the encoder to have the ability to represent local-global features.•We discuss the mask ratio, which is suitable for pathology-specific training methodologies based on the masked image modeling paradigm.•We selected three pathological image datasets and proved the effectiveness of GCMAE algorithm through extensive experiments.•An automatic pathology image diagnosis process was designed based on the GCMAE to improve the credibility of the model in clinical applications.