Diagnose with Uncertainty Awareness: Diagnostic Uncertainty Encoding Framework for Radiology Report Generation

Automated generation of radiology reports from X-ray images serves as a crucial task to streamline the diagnostic workflow for medical imaging and enhance the efficiency of radiologist decision-making. For clinical accuracy, most existing approaches focus on achieving accurate predictions of the existence of abnormalities, despite the inherent uncertainty impacting the reliability of the generated report, which is often clarified by radiologists simultaneously. In this paper, we present a unified report generation framework featuring a novel diagnostic uncertainty estimation model, named Diagnostic Uncertainty Encoding framework (DiagUE). Inspired by the clinician's uncertainty-aware radiology decision-making behavior, DiagUE first formulates belief-based diagnostic uncertainty metrics that effectively capture the variability of radiology abnormalities. Then, the estimated uncertainty-aware abnormality prediction is integrated with a report generation model under a novel visual-language encoding mechanism. Extensive experiments on two public benchmark datasets demonstrate that DiagUE could outperform SOTA baselines in ensuring the clinical accuracy of both abnormality description and diagnostic uncertainty of the report generation.