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准备数据的人工智能在病理学与临床级性能.

Yuanqing Yang1,2, Kai Sun1,3, Yanhua Gao4

  • 1Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China.

Diagnostics (Basel, Switzerland)
|October 14, 2023
PubMed
概括
此摘要是机器生成的。

病理学的人工智能 (AIP) 显示出希望,但在临床环境中面临挑战. 强大的数据准备,标准化和整个幻灯片图像 (WSI) 分析是改善AIP的关键.

关键词:
在病理学中的人工智能.临床级别的临床级别的数据准备数据准备深度学习是一种深度学习.

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科学领域:

  • 数字病理学数字病理学
  • 病理学中的人工智能 (AIP)

背景情况:

  • 病理学依赖于经验丰富的病理学家进行诊断.
  • 病理学中的人工智能 (AIP) 正在出现,以提高准确性和效率.
  • 临床应用AIP在复制实验室性能方面面临挑战.

研究的目的:

  • 从PubMed (118项研究) 审查AIP研究 (2017年1月至2022年2月).
  • 分析AIP的数据准备方法.
  • 确定提高AIP临床绩效的挑战和策略.

主要方法:

  • 对118项AIP研究进行了系统审查.
  • 对数据采集,清理,选,数字化,注释和验证的分析.
  • 调查影响AIP性能可重复性的因素.

主要成果:

  • 数据准备对于AIP的稳定性至关重要.
  • 关键因素包括代表性数据,质量控制,注释和数据量.
  • 数字病理学,数据标准化和基于WSI的弱监督学习是有效的策略.

结论:

  • AIP性能的可复制性取决于代表性数据,适当的标签和多中心一致性.
  • 数字病理学和基于WSI的弱监督学习对于临床级AIP至关重要.
  • 标准化和强大的数据实践对于AIP的成功临床整合至关重要.