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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 may not adequatelyrepresent the entire dataset. Although learning-based stain normalizationmethods are a general approach, they use complex deep networks, which not onlygreatly reduce computational efficiency, but also risk introducing artifacts.Some studies use specialized network structures to enhance computationalefficiency and reliability, but these methods are difficult to apply tomulti-to-one stain normalization due to insufficient network capacity. In thisstudy, we introduced dynamic-parameter network and proposed a novel method forstain normalization, called ParamNet. ParamNet addresses the challenges oflimited network capacity and computational efficiency by introducing dynamicparameters (weights and biases of convolutional layers) into the networkdesign. By effectively leveraging these parameters, ParamNet achieves superiorperformance in stain normalization while maintaining computational efficiency.Results show ParamNet can normalize one whole slide image (WSI) of100,000x100,000 within 25s. The code is available at:https://github.com/khtao/ParamNet.

ParamNet: A Dynamic Parameter Network for Fast Multi-to-One Stain Normalization