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It is commonly recognized that color variations caused by differences instains is a critical issue for histopathology image analysis. Existing methodsadopt color matching, stain separation, stain transfer or the combination ofthem to alleviate the stain variation problem. In this paper, we propose anovel Stain-Adaptive Self-Supervised Learning(SASSL) method for histopathologyimage analysis. Our SASSL integrates a domain-adversarial training module intothe SSL framework to learn distinctive features that are robust to both varioustransformations and stain variations. The proposed SASSL is regarded as ageneral method for domain-invariant feature extraction which can be flexiblycombined with arbitrary downstream histopathology image analysis modules (e.g.nuclei/tissue segmentation) by fine-tuning the features for specific downstreamtasks. We conducted experiments on publicly available pathological imageanalysis datasets including the PANDA, BreastPathQ, and CAMELYON16 datasets,achieving the state-of-the-art performance. Experimental results demonstratethat the proposed method can robustly improve the feature extraction ability ofthe model, and achieve stable performance improvement in downstream tasks.

Stain-Adaptive Self-Supervised Learning for Histopathology Image Analysis