StainNet: a fast and robust stain normalization network

Pathological images have large color variabilities due to various factors. These variations hamper the performance of computer-aided diagnosis (CAD) systems. Stain normalization has been used to reduce the color variability and increase the prediction accuracy. Among these algorithms, the conventional methods perform stain normalization on a pixel-by-pixel basis, but estimate stain parameters just relying on one single reference image and thus would incur some inaccurate normalization results. As for the current deep learning-based methods, the color distribution extraction can be automatically extracted and need not pick a representative reference image. At the same time, the network of deep learning-based methods has a complex structure with millions of parameters, so they have a low computational efficiency and risk to introduce artifacts. In this paper, a fast and robust stain normalization network with only 1.28K parameters named StainNet is proposed. StainNet can learn the color mapping relationship from a whole dataset and adjust the color value in a pixel-to-pixel manner. The proposed method performs well in stain normalization and achieves a better accuracy and image quality.