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一个视觉语言基础模型用于计算病理学

Ming Y Lu^1,2,3,4,5, Bowen Chen^1,2, Drew F K Williamson^1,2,3

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

Nature medicine

|March 20, 2024

概括

来自Histopathology标题的对比学习 (CONCH) 是一种新的视觉语言模型,它使用图像和文本来改进病理学中的AI. 它在各种任务上以最少的微调实现了最先进的结果.

科学领域:

数字病理学数字病理学
人工智能在医学中的应用
计算病理学计算病理学

背景情况:

深度学习模型正在推进数字病理学,但面临诸如有限的标记数据和特定任务培训等挑战.
目前的基因病理学模型主要使用图像数据,与集成各种信息的人类推理不同.
在基因病理学中需要多功能的人工智能模型,可以处理多个任务并利用视觉和文本数据.

研究的目的:

介绍来自Histopathology (CONCH) 的标题的对比学习,这是一个新的视觉语言基础模型,用于组织病理学.
解决目前用于病理学的AI模型中的标签稀缺性和任务特异性限制.
开发一种模型,可以从组织病理学图像和生物医学文本中学习.

主要方法:

开发了CONCH,一种视觉语言基础模型,通过对各种组织病理学图像和生物医学文本进行任务不可知预训练.
用了超过117万个图像字幕对用于模型培训.
对各种下游任务的14个不同的基准进行了CONCH的评估.

主要成果:

在多个组织病理学基准测试中,CONCH 展示了最先进的性能.
在组织学图像分类,细分和标题中取得了卓越的结果.
在文本到图像和图像到文本检索任务中表现出强的表现.
验证了该模型的可转移性,用于广泛的下游任务,最小的微调.

结论:

CONCH 代表了比现有的视觉语言模型在病原病学中的显著进步.
该模型能够整合图像和文本数据,这有助于各种机器学习工作流程.
CONCH有可能简化基于AI的病理学应用程序,几乎不需要进一步的监督微调.

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