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通过保护隐私的大型语言模型和多类型注释来增强胸部X射线数据集:以数据为导向的方法来改进分类.

Ricardo Bigolin Lanfredi¹, Pritam Mukherjee¹, Ronald M Summers¹

¹Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bldg 10, Room 1C224D, 10 Center Dr, Bethesda, MD 20892-1182, USA.

Medical image analysis

|November 15, 2024

概括

新的大型语言模型 (LLM) 系统MAPLEZ通过提取超出简单存在的详细发现来增强胸部X射线 (CXR) 报告标签. 这提高了CXR数据的质量和实用性,用于研究和AI开发.

关键词:

标注注释标注注释胸部X射线胸部X射线分类分类分类分类.大型语言模型.医疗报告医疗报告保护隐私 - 保护隐私

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科学领域:

医学成像分析医学成像分析
医疗保健中的人工智能
放射学自然语言处理用于放射学.

背景情况:

目前的胸部X射线 (CXR) 报告标签方法,包括基于规则的系统和监督深度学习,在标签质量和适应性方面存在局限性.
现有的标签商通常只提供二进制存在标签,限制了它们对高级分析和数据集创建的有用性.

研究的目的:

引入MAPLEZ (医疗报告注释与保护隐私的大型语言模型使用快速零射击答案),一种用于从CXR报告中提取和增强发现标签的新方法.
为了证明MAPLEZ能够提取不仅存在/不存在,还可以提取发现的位置,严重程度和放射学家的不确定性.

主要方法:

利用本地可执行的大型语言模型 (LLM) 来从CXR报告中提取详细注释.
评估MAPLEZ在五个测试组中的八个异常的性能,并将其注释质量与现有方法进行比较.

主要成果:

与竞争对手标签商相比,MAPLEZ在分类存在注释的宏观F1分数上升了3.6个百分点 (pp),在位置注释的F1分数上升了20多个百分点.
使用MAPLEZ的增强和多类型注释改善了有限分辨率CXR的概念验证分类质量,显示AUROC的1.1个百分点的增加.

结论:

MAPLEZ显著提高了CXR报告注释的质量和细节,克服了传统方法的局限性.
由MAPLEZ生成的改进的注释有助于下游AI任务的实质性进步,例如图像分类,特别是对于具有有限分辨率的数据集.