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在血液病理学中应用机器学习.

Taher Dehkharghanian1,2, Youqing Mu2, Hamid R Tizhoosh3

  • 1Department of Nephrology, University Health Network, Toronto, Ontario, Canada.

International journal of laboratory hematology
|May 31, 2023
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 正在推动数字病理学,特别是血液病理学,通过帮助诊断工作流. 正在开发ML模型,以应对独特的挑战,并利用骨髓细胞学和组织病理学数据支持病理学家.

关键词:
人工智能的人工智能是人工智能.数字病理学数字病理学血液病理学 血液病理学机器学习是机器学习.整个幻灯片成像的成像.

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科学领域:

  • 数字病理学数字病理学
  • 血液病理学 血液病理学
  • 机器学习应用 机器学习应用

背景情况:

  • 机器学习 (ML) 越来越多地应用于数字病理学,特别是血液病理学.
  • 这些工具旨在通过分析各种数据,包括数字组织图像来支持诊断工作流.
  • 血液病理学与其他病理学子专业相比,为ML提出了独特的挑战.

研究的目的:

  • 讨论血液病理学机器学习的当前趋势.
  • 为血液病理学工作流程审查现有的支持ML的医疗器械.
  • 探索研究趋势,专注于骨髓细胞学和组织病理学.

主要方法:

  • 审查当前的机器学习应用程序和血液病理学研究.
  • 对支持诊断工作流程的支持ML的医疗器械进行分析.
  • 专注于建模病理学家的工作流程,以解决实际问题.

主要成果:

  • 越来越多的ML应用在数字血液病理学中.
  • 开发 ML 工具以支持诊断决策.
  • 确定骨髓分析中的特定研究趋势.

结论:

  • 机器学习具有显著的潜力,可以增强血液病理学诊断.
  • 解决血液病理学的独特挑战是成功采用ML的关键.
  • 过渡到数字病理学有助于整合新的ML工具.