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数字病理学的多域染色规范化:对于整个幻灯片图像的循环一致的对抗网络

Martin J Hetz¹, Tabea-Clara Bucher¹, Titus J Brinker¹

¹Division of Digital Biomarkers for Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Medical image analysis

|April 4, 2024

概括

本研究介绍了MultiStain-CycleGAN用于在整个幻灯片图像中规范化多种组织学染色. 该方法通过减少域偏差来提高计算机辅助诊断的可靠性和患者数据隐私.

科学领域:

数字病理学数字病理学
医学图像分析医学图像分析
计算机辅助诊断计算机辅助诊断

背景情况:

机构间的组织学染色变化挑战了计算机辅助诊断 (CAD) 的可靠性.
病理整片图像 (WSI) 的差异降低了算法性能,并阻碍了癌症诊断应用.
染色差异引入了偏差,在域移动期间对模型性能产生了负面影响.

研究的目的:

为病理性WSIs开发一个多域染色正常化技术.
为了应对医疗中心各种组织学染色的挑战.
提高CAD系统的可靠性和适用性.

主要方法:

提出了MultiStain-CycleGAN,这是一个基于CycleGAN的新型多域染色正常化方法.
修改了CycleGAN以正常化来自不同来源的图像,而不需要重新训练或单独的模型.
通过域分类,瘤分类性能,结构相似度指数 (SSIM) 和弗雷切开始距离 (FID) 进行了广泛的评估.

主要成果:

MultiStain-CycleGAN展示了多域能力,实现了高图像质量.
该方法有效地欺骗了域名分类器,表明成功的规范化.
保持高瘤分类器性能,同时减少域位移.
与其他具有多域能力的方法相比,实现了卓越的性能.

结论:

MultiStain-CycleGAN是一个强大的解决方案,用于在不同的数据集中进行染色规范化.
这种方法通过减轻染色引起的偏差来提高CAD的可靠性.
改进的规范化通过掩盖图像来源来促进更好的患者数据隐私.

关键词:

敌对的网络敌对的网络循环GANAN是一个循环.域名适应领域适应域名转移域名转移域名转移组织病理学组织病理学污点的正常化污点的正常化

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