学习用预训练的编码器总结中国放射学发现
IEEE transactions on bio-medical engineering
|June 14, 2023
概括
这项研究为中国胸部放射学报告引入了一种新的抽象总结方法,增强了计算机辅助诊断. 开发的中国医学BERT (CMBERT) 模型显著提高了总结性能,帮助医生.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 医疗保健中的人工智能
背景情况:
- 放射学报告的自动总结对于减少医生在计算机辅助诊断中的工作量至关重要.
- 现有的以英语为基础的深度学习总结方法由于语料库的局限性,不适合中文报告.
研究的目的:
- 为中国胸部放射学报告开发一种有效的抽象总结方法.
- 为应对缺乏适合现有总结技术的中国医疗机构所带来的挑战.
主要方法:
- 使用中国医疗数据构建一个预训练集体和微调中国胸部放射学报告的集体.
- 引入一个伪总结目标,以任务为导向的预训练来增强编码器初始化.
- 开发和微调中国医学BERT (CMBERT) 模型用于抽象总结.
主要成果:
- 提出的基于CMBERT的方法在现有的抽象总结模型上显示了显著的性能改善.
- 伪总结目标有效地改善了对总结任务的编码器初始化.
- 该方法在克服中国放射学报告总结之前方法的局限性方面被证明是有效的.
结论:
- 开发的方法为中国自动胸部放射学报告总结提供了一个有前途的解决方案.
- 这一进步可以显著减轻计算机辅助诊断中医生的工作量.
- CMBERT模型是处理中国医学文本数据的宝贵工具.
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