StainNet: a fast and robust stain normalization network
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Pathological images may have large variabilities in color intensities due to inconsistencies in staining process, operator ability, and scanner specifications. 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. However, the conventional methods estimate stain parameters from one single reference image, and the current deep learning based methods 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 the 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.
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