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癌症诊断和预后的病理基础模型
Xiyue Wang1,2, Junhan Zhao1,3, Eliana Marostica1,4
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Nature
|September 4, 2024
概括
通过提取关键病理成像特征,一个新的机器学习模型 - - 临床组织病理成像评估基金会 (CHIEF) 提高了癌症诊断. 这种通用框架可以提高各种数据集的准确性,并优于现有的人工智能方法.
科学领域:
- 数字病理学
- 在瘤学中的机器学习
- 计算病理学
背景情况:
- 组织病理学图像评估对于癌症诊断和分类至关重要.
- 目前用于组织病理学的人工智能模型通常缺乏跨不同数据源和群体的概括性.
- 这种限制阻碍了在临床环境中广泛采用和一致的表现.
研究的目的:
- 开发一个通用的,弱监督的机器学习框架,用于使用细胞病理图像进行系统的癌症评估.
- 创建一个可以提取各种病理成像特征的基础模型,使其具有广泛的应用性.
- 为了解决组织病理学数据领域转移的挑战.
主要方法:
- 临床组织病理成像评估基金会 (CHIEF) 模型是使用弱监督的机器学习框架开发的.
- CHIEF采用了两种互补的预训练策略:无监督的预训练用于层特征和弱监督的预训练用于整个幻灯片图案.
- 该模型使用来自19个解剖学部位的60,530个整片图像的大数据集进行训练,并通过来自各种国际队列的19,491个图像进行验证.
主要成果:
- CHIEF成功地提取了用于癌细胞检测,瘤起源识别,分子分析和预后的微观表征.
- 验证表明CHIEF的性能优越,高达36.1%的性能优于最先进的深度学习方法.
- 该模型有效地解决了与不同人群和不同幻灯片准备方法相关的领域转移.
结论:
- CHIEF为癌症的有效数字病理评估提供了可通用的基础.
- 该框架能够处理域位的转移,从而提高其临床实用性.
- 这种方法为改善癌症护理的组织病理学中人工智能的发展提供了有希望的方向.
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