基于检索的适应融合策略,用于生成医疗报告
Yingying Huang1, Yang Si2, Bingliang Hu3
1College of Big Data and Software Engineering, Zhejiang Wanli University, Ningbo 315100, Zhejiang, PR China; Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shaanxi, PR China; Key laboratory of Biomedical Spectroscopy, Xi'an 710119, Shaanxi, PR China.
Computer methods and programs in biomedicine
|July 10, 2025
概括
这项研究引入了一种新的基于检索的自适应融合策略 (RAFS) 来生成医疗报告,提高胸部CT扫描的准确性和临床解释性. 这种新的方法解决了现有方法中的冗余和错误描述问题.
科学领域:
- 医学成像和自然语言处理.
- 医疗保健中的人工智能.
- 临床信息学 临床信息学
背景情况:
- 现有的基于检索的医疗报告生成方法与冗余性和错误描述作斗争,特别是对于高分辨率图像.
- 目前用于报告生成的评估指标往往忽视了错误描述的临床影响.
- 创建了一个具有9个类别和505名患者的高分辨率CT报告数据集.
研究的目的:
- 开发一个改进的基于检索的医疗报告生成策略.
- 为了提高产生的放射学报告的准确性和临床解释性.
- 为医疗报告生成引入更强大的评估指标.
主要方法:
- 拟议的RAFS (基于检索的自适应融合策略) 以动态平衡检索和生成模块.
- 集成了一个注意力机制,用于计算单词相似性和确定检索概率.
- 开发了DICE,一种双视角评估指标,包含正分和错误描述处罚.
主要成果:
- 在CT报告生成中,RAFS在标准指标 (BLEU-4,METEOR,ROUGE_L,CIDEr) 上取得了卓越的性能.
- 拟议的DICE评分平均为64.6,表明临床评价有所改善.
- 在生成准确和临床相关的报告方面,RAFS的表现优于现有方法.
结论:
- RAFS显著提高了生成的医疗报告的临床解释性.
- 该研究强调了自适应融合策略和综合评估指标的重要性.
- 未来的工作重点是改进在生成的报告中对局部病理性病变的表征.
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