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对骨髓的模型不可知二进制补丁分组 整个幻灯片 图像表示

Youqing Mu1, Hamid R Tizhoosh2, Taher Dehkharghanian3

  • 1Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada.

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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
  • 医学中的人工智能

背景情况:

  • 组织病理学,疾病诊断的黄金标准,面临的挑战是效率和一致性,手动分析数字化整个幻灯片图像 (WSIs).
  • 人工智能 (AI) 模型旨在从WSIs创建幻灯片级表示 (单向量),以实现计算病理学任务,如搜索和分类.
  • 有效的幻灯片级表示非常依赖于补丁特征提取和聚合策略.

研究的目的:

  • 引入和评估一种新的二进制补丁分组 (BPG) 方法作为一个插件,以增强骨髓组织病理学中的幻灯片级表示.
  • 调查BPG对WSI检索和分类性能的影响.
  • 为了比较域一般与域特定的特征提取模型和不同的聚合方法与BPG和没有BPG.

主要方法:

  • 提出了二进制补丁分组 (BPG) 步骤,需要最小的病理学家干预,在特征聚合之前排除临床上无关紧要的补丁.
  • 研究了基于卷积和基于注意力的特征提取模型,比较了域一般和域特定的方法.
  • 评估了两个特征聚合方法,包括BPG步骤和没有BPG步骤,以评估其通用性和对表示质量的影响.

主要成果:

  • BPG方法在不同的特征提取和聚合策略中展示了普遍性.
  • 整合BPG导致WSI检索性能提高了4% (平均精度在10).
  • 与没有BPG的管道相比,BPG导致WSI分类准确度 (加权-F1得分) 提高了5%.

结论:

  • 二进制补丁分组 (BPG) 是一种有效的插件,用于提高计算病理学中的幻灯片级表示的质量.
  • 拟议的BPG方法提高了关键WSI分析任务的性能,包括检索和分类.
  • 在使用BPG时,域一般的大型模型与参数化聚合相结合,为生成高质量的幻灯片级表示提供了最佳性能.