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

TL;DR

RTEx is a comprehensive methodology that ranks radiography exams by abnormality likelihood, tags abnormalities, and provides natural language diagnostic explanations, improving prioritization and interpretability in medical imaging.

Contribution

It introduces RTEx, a novel integrated approach for ranking, tagging, and explaining radiography exams, outperforming existing methods in each task.

Findings

RTEx outperforms competitors in ranking accuracy (NDCG@k).

RTEx's tagging component achieves higher F1 scores.

Diagnostic captioning with RTEx improves clinical precision and recall.

Abstract

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.