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Stain normalization often refers to transferring the color distribution ofthe source image to that of the target image and has been widely used inbiomedical image analysis. The conventional stain normalization is regarded asconstructing a pixel-by-pixel color mapping model, which only depends on onereference image, and can not accurately achieve the style transformationbetween image datasets. In principle, this style transformation can be wellsolved by the deep learning-based methods due to its complicated networkstructure, whereas, its complicated structure results in the low computationalefficiency and artifacts in the style transformation, which has restricted thepractical application. Here, we use distillation learning to reduce thecomplexity of deep learning methods and a fast and robust network calledStainNet to learn the color mapping between the source image and target image.StainNet can learn the color mapping relationship from a whole dataset andadjust the color value in a pixel-to-pixel manner. The pixel-to-pixel mannerrestricts the network size and avoids artifacts in the style transformation.The results on the cytopathology and histopathology datasets show that StainNetcan achieve comparable performance to the deep learning-based methods.Computation results demonstrate StainNet is more than 40 times faster thanStainGAN and can normalize a 100,000x100,000 whole slide image in 40 seconds.

StainNet: a fast and robust stain normalization network