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在计算病理学中的聚合方法的聚合.

Mohsin Bilal1, Robert Jewsbury2, Ruoyu Wang2

  • 1Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK; School of Computing, National University of Computer and Emerging Sciences, Islamabad, Pakistan.

Medical image analysis
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概括

本综述探讨了计算病理学 (CPath) 中全幻灯片图像 (WSI) 分析的聚合方法. 它提供了一个框架和比较,以指导未来的WSI级预测建模研究.

关键词:
预测的聚合预测的聚合.计算病理学计算病理学机器学习 机器学习整个幻灯片图像分析.

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科学领域:

  • 数字病理学数字病理学
  • 计算病理学 (CPath) 是一种
  • 机器学习在医学中的应用

背景情况:

  • 整个幻灯片图像 (WSIs) 需要对层预测进行聚合,以便在WSI层进行分析.
  • 计算病理学的现有聚合方法需要系统的审查和比较.

研究的目的:

  • 在CPath中审查和分类WSI分析的聚合方法.
  • 为预测建模提出一个一般的CPath工作流程.
  • 为了指导未来的WSI级预测研究.

主要方法:

  • 对WSI分析的聚合方法的文献综述.
  • 基于数据上下文,计算模块和CPath用例的方法分类.
  • 方法的比较,特别是多个实例的学习,在一个特定的WSI级预测任务.

主要成果:

  • 一个拟议的一般CPath工作流程,有三个路径.
  • 各种聚合技术的分类和比较.
  • 确定不同聚合方法的目标,理想属性,优缺点.

结论:

  • 聚合方法对于CPath中WSI级预测至关重要.
  • 需要一个结构化的方法和公平的比较来推进这个领域.
  • 提供了关于CPath聚合方法的建议和未来研究方向.