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