伪标签和以知识为导向的对比学习用于放射学报告生成
Fan Ye1, Xuan Hu1, Yihao Ding1
1School of Computer Science and Technology, Anhui University, Hefei, 230601, Anhui Province, China.
Journal of biomedical informatics
|October 24, 2025
概括
这项研究引入了一种新的放射学报告生成 (RRG) 框架,可以提高语义一致性和临床准确性. 通过更好地将图像特征与文本描述对齐,PKCL框架增强了诊断解释.
科学领域:
- 医疗成像中的人工智能
- 为医疗保健提供自然语言生成.
- 深度学习用于放射学.
背景情况:
- 放射学报告生成 (RRG) 旨在提高诊断解释的一致性和全面性.
- 目前基于图形的RRG方法在语义特征-文本对齐,杂的注释和解剖学约束方面存在局限性.
- 解决这些局限性对于推进自动化医疗报告至关重要.
研究的目的:
- 为放射学报告生成提出伪标签和知识导向比较学习 (PKCL) 框架.
- 为了克服语义分离,注释噪声和现有的RRG方法中缺乏解剖学约束.
- 为了提高产生的放射学报告的临床准确性和语义一致性.
主要方法:
- 开发了一个PKCL框架,整合了动态查询学习和以知识为导向的对比学习.
- 采用可训练的交叉模式查询矩阵 (QM) 来通过自我注意机制共享表示.
- 利用伪标签,自适应特征融合和知识图 (XRayVision) 来改善学习和解剖约束.
主要成果:
- 对于生成和临床指标,PKCL在IU-Xray和MIMIC-CXR数据集上实现了最先进的性能.
- 优于R2GEN和CMCL等以前的方法,获得更高的BLEU-1和RL分数.
- 证明了对域外数据集的强大泛化,即使在资源有限的条件下.
结论:
- 通过保持语义一致性,PKCL框架显著提升了放射学报告的生成.
- 它有效地捕捉了放射学发现和文本描述之间的微妙关系.
- 代表了与临床相关报告生成现有方法相比的大幅改进.
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