{"title":"An open chest X-ray dataset with benchmarks for automatic radiology report generation in French","authors":"","doi":"10.1016/j.neucom.2024.128478","DOIUrl":null,"url":null,"abstract":"<div><p>Medical report generation (MRG), which aims to automatically generate a textual description of a specific medical image (e.g., a chest X-ray), has recently received increasing research interest. Building on the success of image captioning, MRG has become achievable. However, generating language-specific radiology reports poses a challenge for data-driven models due to their reliance on paired image-report chest X-ray datasets, which are labor-intensive, time-consuming, and costly. In this paper, we introduce a chest X-ray benchmark dataset, namely <span>CASIA-CXR</span>, consisting of high-resolution chest radiographs accompanied by narrative reports originally written in French. To the best of our knowledge, this is the first public chest radiograph dataset with medical reports in this particular language. Importantly, we propose a simple yet effective multimodal encoder–decoder contextually-guided framework for medical report generation in French. We validated our framework through intra-language and cross-language contextual analysis, supplemented by expert evaluation performed by radiologists. The dataset is freely available at: <span><span>https://www.casia-cxr.net/</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurocomputing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925231224012499","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Medical report generation (MRG), which aims to automatically generate a textual description of a specific medical image (e.g., a chest X-ray), has recently received increasing research interest. Building on the success of image captioning, MRG has become achievable. However, generating language-specific radiology reports poses a challenge for data-driven models due to their reliance on paired image-report chest X-ray datasets, which are labor-intensive, time-consuming, and costly. In this paper, we introduce a chest X-ray benchmark dataset, namely CASIA-CXR, consisting of high-resolution chest radiographs accompanied by narrative reports originally written in French. To the best of our knowledge, this is the first public chest radiograph dataset with medical reports in this particular language. Importantly, we propose a simple yet effective multimodal encoder–decoder contextually-guided framework for medical report generation in French. We validated our framework through intra-language and cross-language contextual analysis, supplemented by expert evaluation performed by radiologists. The dataset is freely available at: https://www.casia-cxr.net/.
医学报告生成(MRG)旨在自动生成特定医学影像(如胸部 X 光片)的文本描述,近来受到越来越多的研究关注。在图像字幕成功的基础上,MRG 已经可以实现。然而,生成特定语言的放射学报告对数据驱动模型构成了挑战,因为它们依赖于成对的图像-报告胸部 X 光数据集,而这些数据集耗费大量人力、时间和成本。在本文中,我们介绍了一个胸部 X 光基准数据集,即 CASIA-CXR,该数据集由高分辨率胸部 X 光片组成,并附有最初以法语撰写的叙述性报告。据我们所知,这是首个以这种特殊语言编写医疗报告的公开胸部 X 光片数据集。重要的是,我们提出了一个简单而有效的多模态编码器-解码器语境引导框架,用于生成法语医疗报告。我们通过语言内和跨语言语境分析验证了我们的框架,并由放射科专家进行了专家评估。数据集可在以下网址免费获取:https://www.casia-cxr.net/。
期刊介绍:
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.