Stain variations often decrease the generalization ability of deep learning
based approaches in digital histopathology analysis. Two separate proposals,
namely stain normalization (SN) and stain augmentation (SA), have been
spotlighted to reduce the generalization error, where the former alleviates the
stain shift across different medical centers using template image and the
latter enriches the accessible stain styles by the simulation of more stain
variations. However, their applications are bounded by the selection of
template images and the construction of unrealistic styles. To address the
problems, we unify SN and SA with a novel RandStainNA scheme, which constrains
variable stain styles in a practicable range to train a stain agnostic deep
learning model. The RandStainNA is applicable to stain normalization in a
collection of color spaces i.e. HED, HSV, LAB. Additionally, we propose a
random color space selection scheme to gain extra performance improvement. We
evaluate our method by two diagnostic tasks i.e. tissue subtype classification
and nuclei segmentation, with various network backbones. The performance
superiority over both SA and SN yields that the proposed RandStainNA can
consistently improve the generalization ability, that our models can cope with
more incoming clinical datasets with unpredicted stain styles. The codes is
available at this https URL

通过 RandStainNA 方案，将 stain normalization (SN) 和 stain augmentation (SA) 相结合，以训练无差别染色的深度学习模型，从而提高数字组织病理学分析中的泛化能力。