In practice, digital pathology images are often affected by various factors,
resulting in very large differences in color and brightness. Stain
normalization can effectively reduce the differences in color and brightness of
digital pathology images, thus improving the performance of computer-aided
diagnostic systems. Conventional stain normalization methods rely on one or
several reference images, but one or several images are difficult to represent
the entire dataset. Although learning-based stain normalization methods are a
general approach, they use complex deep networks, which not only greatly reduce
computational efficiency, but also risk introducing artifacts. StainNet is a
fast and robust stain normalization network, but it has not a sufficient
capability for complex stain normalization due to its too simple network
structure. In this study, we proposed a parameter-variable stain normalization
network, ParamNet. ParamNet contains a parameter prediction sub-network and a
color mapping sub-network, where the parameter prediction sub-network can
automatically determine the appropriate parameters for the color mapping
sub-network according to each input image. The feature of parameter variable
ensures that our network has a sufficient capability for various stain
normalization tasks. The color mapping sub-network is a fully 1x1 convolutional
network with a total of 59 variable parameters, which allows our network to be
extremely computationally efficient and does not introduce artifacts. The
results on cytopathology and histopathology datasets show that our ParamNet
outperforms state-of-the-art methods and can effectively improve the
generalization of classifiers on pathology diagnosis tasks. The code has been
available at this https URL

本研究提出了基于参数可变的深度学习网络 ParamNet，用于数字病理学图像的染色归一化，该网络能够提高诊断系统的性能和分类器的泛化能力，同时保持计算效率和图像质量。