污点SAN:用于组织病理学图像的同时增强和正常化
Taebin Kim1, Yao Li1, Benjamin C Calhoun2,3
1University of North Carolina at Chapel Hill, Department of Statistics and Operations Research, Chapel Hill, North Carolina, United States.
Journal of medical imaging (Bellingham, Wash.)
|August 26, 2024
概括
斑点同时增大和正常化 (SAN) 通过增强斑点颜色的一致性来改善组织病理学图像分析. 这种方法显著提高了分类性能,超过了现有的染色适应技术.
科学领域:
- 数字病理学数字病理学
- 计算性组织病理学
- 医学图像分析 医学图像分析
背景情况:
- 组织病理学图像对于疾病诊断至关重要.
- 对这些图像的计算分析对于临床任务至关重要.
- 在不同实验室和批量中染色的变化构成了重大挑战 (域移).
研究的目的:
- 开发一种有效的染色域适应方法,用于组织病理学.
- 为了提高染色颜色的一致性,并最大限度地减少域间隙.
- 提高下游计算任务的性能,特别是分类.
主要方法:
- 引入了斑点同时增大和正常化 (SAN).
- SAN调整着染色颜色分布,使其与目标分布对齐.
- 结合了染色正常化,增强和混合,同时解决了局限性.
主要成果:
- 污点SAN在跨数据集雌激素受体状态分类中表现出卓越的表现.
- 在一个案例中,AUC增加了11.4%.
- 在没有广泛的培训的情况下,超越现有方法和匹配最先进的基于GAN的方法.
结论:
- 染色SAN是一种强大而有效的方法,用于染色域适应在组织病理学.
- 为诸如分类之类的计算任务提供了显著的改进.
- 提出了一个有前途的解决方案,以克服当前染色适应技术的局限性.
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