论文标题
RTEX：一种用于排名，标记和解释性诊断射线照相考试的新方法
RTEX: A novel methodology for Ranking, Tagging, and Explanatory diagnostic captioning of radiography exams
论文作者
论文摘要
本文介绍了RTEX，这是一种新的方法，用于a）基于其包含异常的可能性对X射线照相检查进行排名，b）为异常检查产生异常标签，c）为每种异常检查提供自然语言的诊断解释。对于想要识别和优先考虑更可能包含异常的射线照相考试的从业者来说，排名射线照相考试的任务是重要的第一步，例如，以避免因疲倦或管理重量工作量而导致的错误（例如，在大流行期间）。我们使用两个公开可用的数据集来评估我们的方法论，并证明，对于NDCG@K而言，将其排名的任务优于其竞争对手。对于每个异常射线照相考试，RTEX都会生成一组异常标签，并在解释性诊断文本以及指导医学专家的解释性诊断文本旁边。我们的标签组件在F1方面优于两种强大的竞争对手方法。此外，RTEX的诊断字幕部分利用已经提取的标签来限制字幕过程，在临床精度和召回方面都优于所有竞争者。
This paper introduces RTEx, a novel methodology for a) ranking radiography exams based on their probability to contain an abnormality, b) generating abnormality tags for abnormal exams, and c) providing a diagnostic explanation in natural language for each abnormal exam. The task of ranking radiography exams is an important first step for practitioners who want to identify and prioritize those radiography exams that are more likely to contain abnormalities, for example, to avoid mistakes due to tiredness or to manage heavy workload (e.g., during a pandemic). We used two publicly available datasets to assess our methodology and demonstrate that for the task of ranking it outperforms its competitors in terms of NDCG@k. For each abnormal radiography exam RTEx generates a set of abnormality tags alongside an explanatory diagnostic text to explain the tags and guide the medical expert. Our tagging component outperforms two strong competitor methods in terms of F1. Moreover, the diagnostic captioning component of RTEx, which exploits the already extracted tags to constrain the captioning process, outperforms all competitors with respect to clinical precision and recall.
