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MOTH:使用QuPath对组织学图像注释进行记忆效率高效的飞行.

Thomas Kauer1, Jannik Sehring1, Kai Schmid1

  • 1Institute of Neuropathology, Justus-Liebig-University Giessen, Arndtstr. 16, 35392 Giessen, Germany.

Journal of imaging
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概括

本研究介绍了一套工具,用于从QuPath项目中无地提取数据,用于对数字遗传病理图像进行人工智能 (AI) 分析. 它简化了人工智能工作流程,通过启用即时注释提取和结果可视化.

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这里是Qupath的路径.细分化 细分化的细分化整个幻灯片图像 整体幻灯片图像

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科学领域:

  • 数字病理学数字病理学
  • 计算病理学计算病理学
  • 人工智能在医学中的应用

背景情况:

  • 数字化组织病理图像为数据分析提供了新的可能性.
  • 人工智能 (AI) 算法可以自动检测和分析整个幻灯片图像中的特征.
  • 从像QuPath这样的注释工具手动提取人工智能训练的数据是耗时的.

研究的目的:

  • 为AI算法开发一个工具包,从QuPath项目中高效地提取数据.
  • 为了促进AI模型培训的注释的飞行提取.
  • 将AI分析结果集成到QuPath中进行视觉检查.

主要方法:

  • 开发一个工具包,用于与现有的AI管道 (如U-net) 集成.
  • 从QuPath项目中快速提取注释.
  • 人工智能结果直接转移回QuPath进行可视化.

主要成果:

  • 该工具包允许直接使用QuPath注释块作为AI算法的输入.
  • 人工智能算法的结果可以在QuPath中直接可视化.
  • 简化工作流程,将QuPath集成到人工智能驱动的组织病理学分析中.

结论:

  • 开发的工具包大大简化了将QuPath集成到AI工作流中的过程.
  • 机动数据提取和结果可视化提高了数字病理学分析的效率.
  • 这种方法促进了人工智能的使用,用于对基因病理学数据的自动分析.