采用X射线图 (PASTER) 进行小样本培训的预训方法:通过胸部X射线图和自由文本报告训练的多式变压器
Kai-Chieh Chen1, Matthew Kuo2, Chun-Ho Lee3
1Graduate Institute of Life Sciences, College of Biomedical Sciences, National Defense Medical University, Taipei, Taiwan, R.O.C.
Journal of medical systems
|September 29, 2025
概括
这项研究表明,一种新的AI模型可以在有限的数据中有效地解释胸部X射线. 多式变压器模型实现了高精度,减少了对医疗成像中大型,专家注释的数据集的需求.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 放射学 放射学是一门学科.
背景情况:
- 深度卷积神经网络 (DCNNs) 需要大型注释数据集来进行胸部X射线 (CXR) 解释.
- 临床数据收集是具有挑战性的,因为资源有限,隐私和患者的变化.
研究的目的:
- 为了评估一个在自由文本报告和CXR中预先训练的多式联网变压器,用于有限数据设置.
- 评估模型在减少样本大小要求方面的有效性,以评估CXR解释中的AI进步.
主要方法:
- 应用了一台多式变压器,在超过100万个CXR和相关放射科医生报告上进行了预训练.
- 从预训练模型中对嵌入式进行训练的线性模型进行了评估.
- 在结构化心声学报告和外部验证集 (CheXpert,胸部X射线14) 的子集上测试了该方法.
主要成果:
- 线性模型使用有限的数据 (128例,384对照) 实现了0.907 (内部) 和0.903 (外部) 的AUC.
- 性能与在整个数据集上训练的DenseNet相似 (AUC为0.908和0.903).
- 在外部验证套件上表现出极好的小样本学习能力.
结论:
- 多式联网变压器模型在有限数据场景中有效地解释CXR.
- 这种方法显著减少了开发CXR解释AI所需的样本大小.
- 该模型显示出在医学成像分析中具有更广泛应用的前景,使用有限的注释数据.
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