自主监督的学习与放射学报告,对大血管占用和大脑CTA图像的策略进行比较分析
1School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, TX 77030.
Proceedings. IEEE International Symposium on Biomedical Imaging
|September 15, 2023
概括
医学成像模型从使用放射学报告的自我监督预训中受益. 将显式放射学概念与CLIP策略相结合,显著改善了大脑CTA中的大血管封闭检测.
科学领域:
- 医学成像医学成像
- 深度学习是一种深度学习.
- 自然语言处理自然语言处理.
背景情况:
- 标签的稀缺性阻碍了对医疗图像的深度学习模型培训.
- 伴随医疗图像的放射学报告为预培训提供了有价值的信息.
- 自主监督学习可以利用未标记的数据进行模型预训练.
研究的目的:
- 为了比较三种自我监督的策略,在3D脑电脑断层扫描血管图 (CTA) 图像上预训练成像模型.
- 评估自然语言处理 (NLP) 方法在从放射学报告中提取信息的有效性.
- 通过使用预先训练的模型,评估大船舶封闭 (LVO) 检测的性能改进.
主要方法:
- 在1,542个未标记的3D CTA-报告对上进行预训练,使用三个自我监督的策略.
- 评估NLP方法:Rad-SpatialNet用于明确的概念和DistilBERT用于报告嵌入.
- 使用对比的语言图像预训练 (CLIP) 方法来学习联合表示.
- 对402名受试者的标记数据集进行微调和测试,以检测LVO.
主要成果:
- 基于CLIP的预训练策略改善了LVO检测的成像模型性能,而不是仅在标记数据上进行训练.
- 使用明确放射学概念与CLIP相结合的预训练产生了最佳表现.
- 该研究表明,利用放射学报告提高医学图像分析的潜力.
结论:
- 使用放射学报告进行自我监督的预训是克服医学成像中标签短缺的可行策略.
- 将显式放射学概念与基于CLIP的学习相结合,为下游医疗图像分析任务 (如LVO检测) 提供了强大的方法.
- 未来的工作可以探索NLP的进一步优化和医疗应用的自我监督技术.
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