arXiv 2305.065112024-07-16eess.IVcs.CV

ParamNet：一种用于快速多对一染色归一化的动态参数网络

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

Southern Medical University（南方医科大学）；Huazhong University of Science and Technology（华中科技大学）；Tongji Hospital（同济医院）；Hubei Maternal and Child Health Hospital（湖北省妇幼保健院）

Hongtao Kang, Die Luo, Li Chen, Junbo Hu, Tingwei Quan, Shaoqun Zeng, Shenghua Cheng, Xiuli Liu

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AI总结：

针对数字病理图像染色差异大且现有方法效率低或容量不足的问题，提出动态参数网络ParamNet，通过引入卷积层动态权重和偏置，实现高效多对一染色归一化，可在25秒内处理10万×10万像素的WSI。

摘要：

在实际应用中，数字病理图像常受多种因素影响，导致颜色和亮度差异极大。染色归一化能有效减小数字病理图像的颜色和亮度差异，从而提高计算机辅助诊断系统的性能。传统染色归一化方法依赖一张或几张参考图像，但一张或几张图像可能无法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方法，但它们使用复杂的深度网络，这不仅大大降低了计算效率，还有引入伪影的风险。一些研究使用专门的网络结构来提高计算效率和可靠性，但这些方法由于网络容量不足，难以应用于多对一染色归一化。在本研究中，我们引入了动态参数网络，并提出了一种新的染色归一化方法，称为ParamNet。ParamNet通过在网络设计中引入动态参数（卷积层的权重和偏置），解决了网络容量有限和计算效率的挑战。通过有效利用这些参数，ParamNet在染色归一化中实现了优越的性能，同时保持了计算效率。结果表明，ParamNet可以在25秒内归一化一张100,000x100,000的全切片图像（WSI）。代码可在以下网址获取：https://github.com/khtao/ParamNet。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。