类-M:基于自适应性染色分离的对比学习与伪标签,用于基因病学图像分类
Bodong Zhang1, Hamid Manoochehri1, Man Minh Ho2
1Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
Medical image analysis
|July 18, 2025
概括
这项研究介绍了CLASS-M,这是一个半监督的模型,用于补丁级异位病理图像分类. 它在清细胞细胞癌数据集上实现了最高性能,而不需要广泛的标签.
科学领域:
- 数字病理学数字病理学
- 医疗图像分析 医疗图像分析
- 计算生物学是一种计算生物学.
背景情况:
- 组织病理图像分类在医学分析中至关重要.
- 由于易于获得的案例级标签,弱监督学习很常见.
- 补丁级分类对于有限的数据集或关键的本地预测至关重要,但需要广泛的标记数据.
研究的目的:
- 提出一种新型的半监督模型,Class-M,用于补丁级的病理学图像分类.
- 为了应对获得广泛标记的数据集用于培训的挑战.
- 在有限或局部标签可用性的场景中提高分类准确性.
主要方法:
- 开发了CLASS-M,这是一个半监督的模型,用于补丁级异位病理图像分类.
- 采用对比式学习模块,使用适应性染色分离用于血素和素图像.
- 集成了一个伪标签模块与MixUp数据增强.
主要成果:
- 与最先进的模型相比,CLASS-M在两个清细胞细胞癌数据集上表现出更高的性能.
- 该模型有效地执行补丁级分类,而不需要大规模的标记数据集.
- 在两个评估数据集中实现了最佳性能指标.
结论:
- CLASS-M提供了一种有效的半监督方法,用于补丁级异位病理图像分类.
- 该模型克服了与广泛的数据标签要求相关的局限性.
- 这种方法显示了提高数字病理学诊断准确性的巨大潜力.
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