计算机工程与应用2025，Vol.61Issue(10)：320-330,11.DOI:10.3778/j.issn.1002-8331.2402-0218
融合多视图特征的放射学报告生成
Radiology Report Generation Integrating Multi-View Features
摘要
Abstract
Radiology report generation involves extracting features from multiple source images and converting them into textual descriptions.The current research faces challenges related to multiple views and varying report lengths,resulting in insufficient accuracy and semantic incoherence in the generated clinical reports.To address these issues,a method that integrates features from multiple views is proposed,reducing information loss through multiple local feature extractions and fine-grained fusion from original images.Global context representation is obtained and embedded by using annota-tion tools,allowing the model to use more comprehensive text during training for smoother descriptions.Experiments on IU X-Ray and MIMIC-CXR datasets show an average improvement of 2.96 percentage points in report quality scores with the application of this method on the R2Gen model.Furthermore,experiments on a self-constructed Chinese lung CT report dataset for image report to diagnostic conclusion generation demonstrate the generality of the proposed method.关键词
放射学报告生成/多视图/细粒度融合/全局上下文Key words
radiology report generation/multiple views/fine-grained fusion/global context分类
计算机与自动化引用本文复制引用
欧佳乐,昝红英,张坤丽,师相龙,马玉团..融合多视图特征的放射学报告生成[J].计算机工程与应用,2025,61(10):320-330,11.基金项目
郑州市协同创新重大专项(20XTZX11020). （20XTZX11020）
