{"title":"SGRRG: Leveraging radiology scene graphs for improved and abnormality-aware radiology report generation","authors":"Jun Wang , Lixing Zhu , Abhir Bhalerao , Yulan He","doi":"10.1016/j.compmedimag.2025.102644","DOIUrl":null,"url":null,"abstract":"<div><div>Radiology report generation (RRG) methods often lack sufficient medical knowledge to produce clinically accurate reports. A scene graph provides comprehensive information for describing objects within an image. However, automatically generated radiology scene graphs (RSG) may contain noise annotations and highly overlapping regions, posing challenges in utilizing RSG to enhance RRG. To this end, we propose Scene Graph aided RRG (SGRRG), a framework that leverages an automatically generated RSG and copes with noisy supervision problems in the RSG with a transformer-based module, effectively distilling medical knowledge in an end-to-end manner. SGRRG is composed of a dedicated scene graph encoder responsible for translating the radiography into a RSG, and a scene graph-aided decoder that takes advantage of both patch-level and region-level visual information and mitigates the noisy annotation problem in the RSG. The incorporation of both patch-level and region-level features, alongside the integration of the essential RSG construction modules, enhances our framework’s flexibility and robustness, enabling it to readily exploit prior advanced RRG techniques. A fine-grained, sentence-level attention method is designed to better distill the RSG information. Additionally, we introduce two proxy tasks to enhance the model’s ability to produce clinically accurate reports. Extensive experiments demonstrate that SGRRG outperforms previous state-of-the-art methods in report generation and can better capture abnormal findings. Code is available at <span><span>https://github.com/Markin-Wang/SGRRG</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102644"},"PeriodicalIF":4.9000,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computerized Medical Imaging and Graphics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0895611125001533","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Radiology report generation (RRG) methods often lack sufficient medical knowledge to produce clinically accurate reports. A scene graph provides comprehensive information for describing objects within an image. However, automatically generated radiology scene graphs (RSG) may contain noise annotations and highly overlapping regions, posing challenges in utilizing RSG to enhance RRG. To this end, we propose Scene Graph aided RRG (SGRRG), a framework that leverages an automatically generated RSG and copes with noisy supervision problems in the RSG with a transformer-based module, effectively distilling medical knowledge in an end-to-end manner. SGRRG is composed of a dedicated scene graph encoder responsible for translating the radiography into a RSG, and a scene graph-aided decoder that takes advantage of both patch-level and region-level visual information and mitigates the noisy annotation problem in the RSG. The incorporation of both patch-level and region-level features, alongside the integration of the essential RSG construction modules, enhances our framework’s flexibility and robustness, enabling it to readily exploit prior advanced RRG techniques. A fine-grained, sentence-level attention method is designed to better distill the RSG information. Additionally, we introduce two proxy tasks to enhance the model’s ability to produce clinically accurate reports. Extensive experiments demonstrate that SGRRG outperforms previous state-of-the-art methods in report generation and can better capture abnormal findings. Code is available at https://github.com/Markin-Wang/SGRRG.
期刊介绍:
The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.