StainNet: a fast and robust stain normalization network

• URL: http://arxiv.org/abs/2012.12535v5
• Date: Sat, 16 Jan 2021 05:26:38 GMT
• Title: StainNet: a fast and robust stain normalization network
• Authors: Hongtao Kang, Die Luo, Weihua Feng, Junbo Hu, Shaoqun Zeng, Tingwei Quan, and Xiuli Liu
• Abstract summary: This paper proposes a fast and robust stain normalization network with only 1.28K parameters named StainNet. The proposed method performs well in stain normalization and achieves a better accuracy and image quality.
• Score: 0.7796684624647288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to a variety of factors, pathological images have large color variabilities, which hamper the performance of computer-aided diagnosis (CAD) systems. Stain normalization has been used to reduce the color variability and increase the accuracy of CAD systems. Among them, 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, it can automatically extract the color distribution and need not pick a representative reference image. While the deep learning-based methods have a complex structure with millions of parameters, and a relatively low computational efficiency and a 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. Application results show the cervical cytology classification achieved a higher accuracy when after stain normalization of StainNet.

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