通过利用热启动来改善胸部X射线报告的生成
Aaron Nicolson1, Jason Dowling1, Bevan Koopman1
1The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Australia.
Artificial intelligence in medicine
|October 2, 2023
概括
热启动深度学习模型与预训练的检查点显著改善胸部X射线 (CXR) 报告生成准确性. CvT2DistilGPT2模型显示出卓越的性能,增强了临床应用的诊断能力.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 自然语言处理自然语言处理.
背景情况:
- 自动胸部X射线 (CXR) 报告生成旨在减少临床工作量并提高患者护理.
- 目前用于CXR报告生成的编码器到解码器模型缺乏足够的诊断准确性,无法在临床部署.
研究的目的:
- 为了提高自动CXR报告生成的诊断准确度.
- 调查热启动编码器和解码器模型与预先训练的检查点的有效性.
主要方法:
- 利用像Vision Transformer (ViT) 这样的开源计算机视觉检查点和像PubMedBERT这样的自然语言处理检查点进行热启动.
- 在MIMIC-CXR和IU X射线数据集上评估检查点的性能.
- 将拟议的CvT2DistilGPT2模型与最先进的M2变压器progressive进行了比较.
主要成果:
- 卷积视觉变压器 (CvT) ImageNet-21K和蒸生成预训练变压器2 (DistilGPT2) 被确定为分别为编码器和解码器热启动的最佳检查点.
- 与M2变压器Progressive相比,CvT2DistilGPT2模型取得了显著的改进:CE F-1中的8.3%,BLEU-4中的1.8%,ROUGE-L中的1.6%,METEOR中的1.0%.
- 生成的报告与放射科医生报告的相似性更高.
结论:
- 热启动的编码器和解码器模型具有适当的预训练检查点,可以大大提高自动CXR报告生成的性能.
- CvT2DistilGPT2模型代表了从CXRs生成临床相关和准确的放射学报告的重大进步.
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