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走向计算机病理学的通用基础模型.

Richard J Chen1,2,3,4,5, Tong Ding1,6, Ming Y Lu1,2,3,4,7

  • 1Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Nature medicine
|March 20, 2024
PubMed
概括

UNI是一个自我监督的模型,通过从超过1亿张组织图像中学习来增强计算病理学 (CPath). 它在各种CPath任务上实现了最先进的性能,使得用于解剖病理学的数据效率高的AI成为可能.

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科学领域:

  • 计算病理学计算病理学
  • 数字病理学数字病理学
  • 医学中的人工智能

背景情况:

  • 组织病理学图像的定量评估对于计算病理学 (CPath) 是至关重要的.
  • 高分辨率的全幻灯片图像 (WSIs) 和特征变化给AI带来了注释挑战.
  • 现有的转移学习和自我监督方法缺乏跨组织类型的大规模,多样化的评估.

研究的目的:

  • 介绍UNI,一种通用的病理学自我监督模型.
  • 在H&E染色的WSIs的大型数据集上预训练UNI,以便广泛适用.
  • 评估UNI在广泛的CPath任务上的性能和新能力.

主要方法:

  • 开发了UNI,一种自我监督的病理学模型.
  • 在20种组织类型的1亿多个H&E染色WSI (>77TB) 上接受过预先训练的UNI.
  • 在34个不同的CPath任务上评估了UNI,包括分类和分类型.

主要成果:

  • 在CPath任务上,UNI的表现优于以前的先进模型.
  • 证明了分辨率不可知组织分类能力.
  • 在几次射击幻灯片分类和疾病亚型 (108种癌症类型) 中取得了强的表现.

结论:

  • 对于CPath来说,UNI代表了大规模无监督表示学习的重大进步.
  • 该模型使数据效率高的AI模型能够在各种任务和临床工作流中进行概括.
  • UNI促进了对解剖病理学的强大的AI工具的开发.