代的多个实例学习对于弱注释的整个幻灯片图像分类
Yuanpin Zhou1, Shuanlong Che2, Fang Lu2
1School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, People's Republic of China.
Physics in medicine and biology
|June 13, 2023
概括
一种新的代多重实例学习 (MIL) 方法改善了全幻灯片图像 (WSI) 在基因病理学中的分类. 这种方法通过改进图像补丁的特征提取来提高诸如肺癌等疾病的诊断准确性.
科学领域:
- 计算病理学计算病理学
- 数字组织病理学 数字组织病理学
- 机器学习在医学中的应用
背景情况:
- 整个幻灯片图像 (WSIs) 对组织病理学至关重要,但它们的高分辨率使详细的注释变得复杂.
- 将WSIs分类通常使用多个实例学习 (MIL),将WSI视为一个实例 (补丁) 的袋子.
研究的目的:
- 开发一种新的代多个实例学习 (IMIL) 方法,仅使用幻灯片级标签来对世界互联网进行分类.
- 通过改进WSI分类来提高组织病理学分析的准确性.
主要方法:
- 提出了一种代的MIL (IMIL) 方法,可以协同学习实例和袋子表示.
- 使用基于注意力的MIL聚合的选择实例和伪标签,实现了特征提取器的代微调.
- 雇员自主监督学习用于特征提取器初始化,基于注意力得分的样本选择,以及对强有力的培训的自信意识损失.
主要成果:
- 在Camelyon16和KingMed-Lung数据集上,IMIL-SimCLR实现了最佳的分类性能,平均AUC高达4.25%超过了CLAM基线.
- 在TCGA-肺部数据集上,IMIL-ImageNet表现出优异的性能,AUC为96.55%和精度为96.76%,超过CLAM的AUC为1.65%和精度为2.09%.
- 在各种WSI分类任务中,IMIL方法在公共和内部数据集中被证明有效.
结论:
- 拟议的代MIL (IMIL) 方法为组织病理学WSI分类提供了重大进展.
- 与最先进的MIL方法相比,IMIL在多个数据集和分类任务中表现出卓越的性能.
- 这种方法有望提高数字病理学的诊断准确性和效率.
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