多尺度动态稀疏令牌多实例学习用于病理学图像分类图像分类
IEEE journal of biomedical and health informatics
|March 3, 2025
概括
这项研究引入了乳腺癌病理图像分析的新框架,改善了全幻灯片图像 (WSIs) 中微妙病变的识别. 动态稀疏令牌和跨度对比学习方法提高了诊断准确度.
科学领域:
- 数字病理学数字病理学
- 计算瘤学计算瘤学
- 机器学习在医学中的应用
背景情况:
- 乳腺癌病理学中的全幻灯片图像 (WSIs) 由于有限的信息瘤区域而存在挑战,这使得病理学家难以识别微妙的病变.
- 诊断需求 (瘤面积<10%) 和WSI数据量之间的信息差距需要先进的计算方法.
研究的目的:
- 开发一个高效的计算框架来分析具有挑战性的乳腺癌病理图像.
- 为了解决WSIs中病变识别的劳动密集性.
主要方法:
- 一个动态稀疏的基于代币的多实例学习框架,在变压器架构中具有动态稀疏层.
- 一个监督较弱的跨度对比学习框架,利用多度病理图像特征进行袋级表示.
主要成果:
- 与最先进的方法相比,拟议的框架在六个评估指标中表现出卓越的表现.
- 在四个癌症数据集上的实验验验证了该模型在单级和多级分析中的有效性和可转移性.
结论:
- 动态稀疏代币和跨度对比学习框架有效地解决了乳腺癌病理图像分析方面的挑战.
- 该模型为识别微妙病变提供了更高的准确性和效率,有助于临床诊断.
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