Our paper focuses on automating the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists. Unlike existing medical re-port generation efforts that tend to produce human-readable reports, we aim to generate medical reports that are both fluent and clinically accurate. This is achieved by our fully differentiable and end-to-end paradigm containing three complementary modules: taking the chest X-ray images and clinical his-tory document of patients as inputs, our classification module produces an internal check-list of disease-related topics, referred to as enriched disease embedding; the embedding representation is then passed to our transformer-based generator, giving rise to the medical reports; meanwhile, our generator also pro-duces the weighted embedding representation, which is fed to our interpreter to ensure consistency with respect to disease-related topics.Our approach achieved promising results on commonly-used metrics concerning language fluency and clinical accuracy. Moreover, noticeable performance gains are consistently ob-served when additional input information is available, such as the clinical document and extra scans of different views.
翻译:我们的文件侧重于从胸前X射线图像输入中生成医疗报告自动化,这是放射学家一项关键而又耗时的任务。与现有的医疗再传送生成努力不同,我们的目标是生成流畅和临床准确的医疗报告,这是通过我们完全不同和端到端的模式实现的,其中包括三个互补模块:将胸前X射线图像和病人临床病历文件作为投入,我们的分类模块编制了一份与疾病有关的题目的内部检查清单,称为“浓缩疾病嵌入”;嵌入表随后传递给基于变压器的发电机,从而产生医疗报告;与此同时,我们的发电机还制作加权嵌入表,供翻译使用,以确保与疾病相关专题保持一致。我们的方法在常用的语言流出率和临床精确度衡量标准方面取得了可喜的成果。此外,在获得更多投入信息,例如临床文件和不同观点的更多扫描时,我们始终看不到显著的业绩成果。