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基于集群的基因组病理学现象型表示学习通过自我监督的多类符号层次ViT.

Jiarong Ye1, Shivam Kalra2, Mohammad Saleh Miri1

  • 1Roche Diagnostics Solutions, Santa Clara, CA, USA.

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|February 8, 2024
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概括

CypherViT是一种新的自我监督学习方法,通过从未标记的图像中学习来增强组织病理学AI模型. 这种方法降低了成本,并提高了癌症亚型等任务的性能.

科学领域:

  • 计算病理学计算病理学
  • 医学中的人工智能
  • 机器学习用于医学成像.

背景情况:

  • 临床AI开发需要广泛的,专家注释的数据集,增加时间和成本.
  • 自主监督学习 (SSL) 利用未标记的数据来构建特定领域的知识,提高AI模型的性能.
  • 组织病理学图像分析对于疾病诊断和研究至关重要.

研究的目的:

  • 介绍CypherViT,一个新的等级视觉转换器 (ViT) 用于在组织病理学中进行自我监督的表示学习.
  • 能够从基因病理图像中提取粗粒和细粒的特征.
  • 开发一种具有成本效益和高效的临床AI模型培训方法.

主要方法:

  • 开发了CypherViT,这是一个基于集群的,自我监督的,多类代币层次的ViT.
  • 将CypherViT集成到DINO SSL框架中,用于培训未标记的乳腺癌组织病理学图像.
  • 采用分层特征聚合注意模块,具有多个分类令牌,用于增强特征学习.

主要成果:

  • CypherViT成功地学习了与形态表型一致的有意义的感兴趣区域.
  • 经过训练的模型证明了作为结直肠癌图像的特征提取器的概括性.
  • 在四个公共数据集的补丁级组织表型化中取得了有前途的表现,超过了现有的SSL和转移学习方法.

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结论:

  • 使用自主监督的方法,CypherViT提供了一种强大的和可泛化的方法,用于对基因病理学表示学习.
  • 这种方法显著减少了对大型注释数据集的依赖,降低了AI开发成本.
  • 与当前最先进的SSL和医疗成像中的传统转移学习技术相比,CypherViT显示出了实质性的优势.