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HistoEM:一种病理学家指导和可解释的工作流程,使用直方图嵌入用于腺体分类.

Alessandro Ferrero1, Elham Ghelichkhan1, Hamid Manoochehri1

  • 1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
|February 18, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了HistoEM,一个框架,使卷积神经网络 (CNN) 能够从数字幻灯片中学习病理学家认可的特征,用于前列腺癌检测和分级. 该模型有效地识别了核特征,反映了人类诊断方法.

关键词:
计算基因病理学计算基因病理学深度学习是一种深度学习.可以解释的人工智能AI前列腺癌是前列腺癌.

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科学领域:

  • 数字病理学数字病理学
  • 计算生物学 计算生物学
  • 在瘤学瘤学.

背景情况:

  • 病理学家使用已确定的标准来诊断和分类前列腺癌.
  • 目前用于前列腺癌检测的卷积神经网络 (CNN) 没有整合这种病理学家衍生的知识.
  • 机器学习衍生特征与病理学家诊断特征之间的对齐仍然不清楚.

研究的目的:

  • 开发一个框架,训练算法来辨别良性和癌性前列腺之间的细胞和亚细胞区别.
  • 调查机器学习模型是否学习与病理学家在前列腺癌分级中使用的特征一致的特征.

主要方法:

  • 一个新的框架,HistoEM,被开发用于分析血素和色素染色数字前列腺组织幻灯片.
  • 该管道涉及精确的腺体细分,流体排除,并利用CNN潜伏空间特征的直方图嵌入.
  • 一个两阶段的网络将腺体分类为良性与癌症,并使用U-Net架构进一步分类癌症腺体 (低级与高级).

主要成果:

  • 该HistoEM模型的性能与最先进的前列腺癌分级模型在腺水平上取得了可比性.
  • 特征分析显示,HistoEM优先考虑核特征,与病理学家的诊断实践保持一致.
  • 像Grad-CAM这样的可视化技术证实了模型的重点是相关的核特性.

结论:

  • HistoEM框架成功地将病理学家认可的特征集成到前列腺癌检测和分级的CNN中.
  • 该模型对核特征的依赖表明了与人类专家解释的趋同.
  • 这种方法提供了一个广泛适用的方法可视化和理解计算机学习的特征在他的病理学.