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In practice, digital pathology images are often affected by various factors,resulting in very large differences in color and brightness. Stainnormalization can effectively reduce the differences in color and brightness ofdigital pathology images, thus improving the performance of computer-aideddiagnostic systems. Conventional stain normalization methods rely on one orseveral reference images, but one or several images are difficult to representthe entire dataset. Although learning-based stain normalization methods are ageneral approach, they use complex deep networks, which not only greatly reducecomputational efficiency, but also risk introducing artifacts. StainNet is afast and robust stain normalization network, but it has not a sufficientcapability for complex stain normalization due to its too simple networkstructure. In this study, we proposed a parameter-variable stain normalizationnetwork, ParamNet. ParamNet contains a parameter prediction sub-network and acolor mapping sub-network, where the parameter prediction sub-network canautomatically determine the appropriate parameters for the color mappingsub-network according to each input image. The feature of parameter variableensures that our network has a sufficient capability for various stainnormalization tasks. The color mapping sub-network is a fully 1x1 convolutionalnetwork with a total of 59 variable parameters, which allows our network to beextremely computationally efficient and does not introduce artifacts. Theresults on cytopathology and histopathology datasets show that our ParamNetoutperforms state-of-the-art methods and can effectively improve thegeneralization of classifiers on pathology diagnosis tasks. The code has beenavailable at https://github.com/khtao/ParamNet.

ParamNet: A Parameter-variable Network for Fast Stain Normalization