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 may not adequately 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. Some studies use specialized network structures to enhance computational efficiency and reliability, but these methods are difficult to apply to multi-to-one stain normalization due to insufficient network capacity. In this study, we introduced dynamic-parameter network and proposed a novel method for stain normalization, called ParamNet. ParamNet addresses the challenges of limited network capacity and computational efficiency by introducing dynamic parameters (weights and biases of convolutional layers) into the network design. By effectively leveraging these parameters, ParamNet achieves superior performance in stain normalization while maintaining computational efficiency. Results show ParamNet can normalize one whole slide image (WSI) of 100,000x100,000 within 25s. The code is available at: https://github.com/khtao/ParamNet.

翻译：在实际应用中，数字病理图像常受多种因素影响，导致颜色与亮度差异极大。染色归一化可有效减少数字病理图像在颜色与亮度上的差异，从而提升计算机辅助诊断系统的性能。传统染色归一化方法依赖于一张或数张参考图像，但单张或少量图像可能不足以充分代表整个数据集。尽管基于学习的染色归一化方法是一种通用途径，但它们使用复杂的深度网络，不仅大幅降低计算效率，还存在引入伪影的风险。部分研究采用专用网络结构以提升计算效率与可靠性，但由于网络容量不足，这些方法难以适用于多对一染色归一化。本研究引入动态参数网络，提出了一种名为ParamNet的新型染色归一化方法。ParamNet通过在网络设计中引入动态参数（卷积层的权重与偏置），解决了网络容量有限与计算效率的挑战。通过有效利用这些参数，ParamNet在保持计算效率的同时，实现了优异的染色归一化性能。实验结果表明，ParamNet可在25秒内完成对整张100,000×100,000像素的全切片图像（WSI）的归一化处理。代码公开于：https://github.com/khtao/ParamNet。