德国CheXpert胸部X射线放射学报告Labeler
Alessandro Wollek1,2, Sardi Hyska3, Thomas Sedlmeyr1,2
1Munich Institute of Biomedical Engineering, Technical University of Munich, Garching b. München, Germany.
概括
一个自动化的算法从德国放射学报告中提取标签,大大改善了胸部X射线分类的深度学习模型. 这种方法为手动注释提供了更快的替代方案,提高了模型性能,并使更大的数据集成为可能.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 机器学习用于医学诊断
背景情况:
- 对于胸部X射线分类的深度学习模型需要大型,准确注释的数据集.
- 放射学报告的手动注释是耗时且资源密集的.
- 开发自动化方法来从报告中提取标签对于高效的模型培训至关重要.
研究的目的:
- 开发一个算法,用于德国胸部放射学报告的自动注释.
- 训练基于深度学习的胸部X射线分类模型,使用自动提取的标签.
- 用自动标签训练的模型的性能与手动和公共数据集进行评估.
主要方法:
- 基于CheXpert架构的自动标签提取模型是为德国报告设计的.
- 创建了一个基于Web的注释界面,用于代改进和基础真相生成.
- 通过使用自动提取的标签,手动标签和用于肺胸部分类的公共数据来训练和评估DenseNet-121模型.
主要成果:
- 自动标签提取实现了高F1评分,用于提及 (0.8-0.995),否定 (0.624-0.981) 和不确定性 (0.353-0.725) 检测.
- 使用自动提取标签进行肺胸部分类,显示高灵敏度 (0.997) 和特异性 (0.991).
- 在自动提取的标签上训练的模型 (AUC 0.858) 优于在公共数据上训练的模型 (AUC 0.728) 并与手动注释 (AUC 0.934) 竞争.
结论:
- 从德国胸部放射学报告中自动提取标签是手动标签的可行和有效的替代方案.
- 这种自动化方法可以创建更大的训练数据集,从而提高深度学习模型的性能.
- 开发的算法在没有额外的注释时间的情况下,在胸部X射线分类任务中提供了竞争力或优异的性能.
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