使用医学Twitter进行病理图像分析的视觉语言基础模型

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Zhi Huang^1,2, Federico Bianchi³, Mert Yuksekgonul³

¹Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.

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概括

研究人员创建了OpenPath,这是一个来自公共论坛的大量病理图像和描述数据集. 他们开发了病理学语言图像预训练 (PLIP),这是一种AI模型,可以显著改善医学AI的图像分类和病例检索.

科学领域:

计算病理学计算病理学
医疗人工智能的人工智能
数据策划数据的策划.

背景情况:

有注释的医学图像很少,阻碍了计算研究和教育.
临床医生在像医疗Twitter这样的公共平台上分享非身份化图像和知识.
众包为策划大规模医疗数据集提供了一个潜在的解决方案.

研究的目的:

来自公共来源的病理学图像的大型注释数据集的策划.
开发一种多式人工智能模型 (PLIP) 用于病理图像分析.
展示策划数据集和开发的AI模型在增强医学诊断和知识共享方面的实用性.

主要方法:

利用公共论坛 (例如,医疗Twitter) 策划OpenPath,这是一个包含208,414个病理图像和自然语言描述的数据集.
开发了病理学语言图像预训练 (PLIP),这是一个在OpenPath数据集上训练的多式人工智能模型.
评估PLIP在外部数据集上的性能,以进行零射击分类,并与现有模型进行比较.

主要成果:

在外部病理图像数据集上,PLIP实现了最先进的零射击分类F1分数 (0.565-0.832),明显优于之前的模型 (0.030-0.481).
与其他监督模型相比,对PLIP嵌入式的监督分类人员的培训使F1得分提高了2.5%.
通过图像或自然语言查询,PLIP展示了有效的案例检索,促进了知识共享.

结论:

在人群平台上公开共享的医疗信息是开发医疗AI的宝贵资源.
开放式数据集和PLIP模型推进了计算病理学研究,教育和诊断能力.
这种方法突显了利用众包数据创造强大的医疗人工智能工具的潜力.

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