一个诊断链框架,用于准确和可解释的放射学报告生成
IEEE transactions on medical imaging
|July 3, 2025
概括
本研究引入了用于可信的放射学报告生成 (RRG) 的诊断链 (CoD) 框架. CoD通过使用诊断对话和接地模块来提高RRG的准确性和可解释性.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 在医疗保健中的自然语言处理.
背景情况:
- 目前的放射学报告生成 (RRG) 模型在临床有效性方面扎,特别是在描述病变属性方面.
- 生成的放射学报告往往缺乏解释性,阻碍了放射科医生的信任.
研究的目的:
- 开发可靠的RRG模型,生成准确的异常描述,并提供预测理由.
- 提高自动化放射学报告的临床有效性和可解释性.
主要方法:
- 提出了一个诊断链 (CoD) 框架,集成诊断对话和大型语言模型.
- 实现了一个诊断接地模块,用于将质量保证诊断与生成句子匹配.
- 开发了一种损伤接地模块,用于查找图像中的异常.
- 引入了一个全方位监督的学习策略,为标签有效的培训提供临床一致性.
主要成果:
- 创建了一个全标RRG数据集,包含问题答案对和损伤框.
- 开发了一种评估工具,用于评估病变位置和严重程度的报告准确性.
- 证明Cod在RRG基准上表现优于专家和通用模型.
- 通过精确地将生成的句子与诊断和图像结合起来,展示了有希望的可解释性.
结论:
- 诊断链 (CoD) 框架为放射学报告生成提供了临床准确和可解释的方法.
- CoD显著提高了自动化放射学报告系统的性能和可靠性.
- 提出的方法促进了高效的培训,并提高了放射科医生工作流程的效率.
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