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Stain variations often decrease the generalization ability of deep learningbased approaches in digital histopathology analysis. Two separate proposals,namely stain normalization (SN) and stain augmentation (SA), have beenspotlighted to reduce the generalization error, where the former alleviates thestain shift across different medical centers using template image and thelatter enriches the accessible stain styles by the simulation of more stainvariations. However, their applications are bounded by the selection oftemplate images and the construction of unrealistic styles. To address theproblems, we unify SN and SA with a novel RandStainNA scheme, which constrainsvariable stain styles in a practicable range to train a stain agnostic deeplearning model. The RandStainNA is applicable to stain normalization in acollection of color spaces i.e. HED, HSV, LAB. Additionally, we propose arandom color space selection scheme to gain extra performance improvement. Weevaluate our method by two diagnostic tasks i.e. tissue subtype classificationand nuclei segmentation, with various network backbones. The performancesuperiority over both SA and SN yields that the proposed RandStainNA canconsistently improve the generalization ability, that our models can cope withmore incoming clinical datasets with unpredicted stain styles. The codes isavailable at https://github.com/yiqings/RandStainNA.

RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization