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This paper introduces RTEx, a novel methodology for a) ranking radiographyexams based on their probability to contain an abnormality, b) generatingabnormality tags for abnormal exams, and c) providing a diagnostic explanationin natural language for each abnormal exam. The task of ranking radiographyexams is an important first step for practitioners who want to identify andprioritize those radiography exams that are more likely to containabnormalities, for example, to avoid mistakes due to tiredness or to manageheavy workload (e.g., during a pandemic). We used two publicly availabledatasets to assess our methodology and demonstrate that for the task of rankingit outperforms its competitors in terms of NDCG@k. For each abnormalradiography exam RTEx generates a set of abnormality tags alongside anexplanatory 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 thealready extracted tags to constrain the captioning process, outperforms allcompetitors with respect to clinical precision and recall.

RTEX: A novel methodology for Ranking, Tagging, and Explanatory diagnostic captioning of radiography exams