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相关实验视频

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Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
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LESS:用于细胞学全幻灯片图像查的标签效率高的多级学习.

Beidi Zhao1, Wenlong Deng1, Zi Han Henry Li2

  • 1Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Vector Institute, Toronto, ON M5G 1M1, Canada.

Medical image analysis
|February 22, 2024
PubMed
概括

我们开发了一种标签高效的WSI选 (LESS) 方法,用于分析仅使用幻灯片级标签的细胞学整片图像 (WSIs). 这种方法提高了准确性和效率,特别是对于小型数据集,使得自动化癌症查成为可能.

关键词:
计算病理学计算病理学多个实例的学习是多个实例的学习.整个幻灯片图像的图像.

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科学领域:

  • 计算病理学计算病理学
  • 数字病理学数字病理学
  • 机器学习用于医学成像.

背景情况:

  • 在计算病理学中,全幻灯片图像 (WSI) 分析面临着大型千兆像素图像的计算挑战.
  • 现有的多个实例学习 (MIL) 方法用于WSI分析,往往忽视特定任务的幻灯片级标签监督,导致低于最佳的特征提取.
  • 在WSIs中进行补丁特征提取的预训练或自我监督模型可能由于这种监督而无效或无效.

研究的目的:

  • 为细胞学WSI分析提出一种弱监督,标签效率高的方法 (LESS),即使数据有限,也有效.
  • 通过整合幻灯片级标签来改善补丁级特征学习,以便更好地对任务进行对齐.
  • 通过在细胞学图像中解决稀疏细胞排列来增强WSI分类.

主要方法:

  • 雇佣的变异性阳性未标记 (VPU) 学习使用幻灯片级监督推断隐藏的补丁标签 (良性/恶性).
  • 实施了多层次的补丁作物策略,以从稀疏的细胞安排中获取信息.
  • 使用交叉注意力视觉变压器 (CrossViT) 集成多尺度补丁信息进行幻灯片级分类.

主要成果:

  • 在尿液和乳腺细胞学WSI数据集上,LESS方法实现了高性能,精度高达96.88%,AUC高达98.95%.
  • 与最先进的MIL方法相比,在病理学WSIs上表现出卓越的性能.
  • 成功启用了自动化细胞学WSI癌症查.

结论:

  • 拟议的LESS方法有效地解决了细胞学WSI分析中的标签效率挑战.
  • 将VPU学习与多尺度CrossViT处理相结合,可以提高特征提取和分类的准确性.
  • LESS提供了一种有前途的解决方案,用于使用WSIs进行自动化癌症查,特别是在资源有限的场景中.