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This paper introduces the Posterior Diagnostic Decision framework (PDD-RRG) to improve automatic radiology report generation (RRG) by incorporating a decision-making stage that resolves conflicting diagnoses. By generating reports from multiple subsets of input data and utilizing Bayesian posterior probabilities, the framework refines the initial reports to enhance diagnostic accuracy. Experiments on the MIMIC-CXR dataset show that PDD-RRG significantly improves the clinical efficacy of existing RRG models without requiring retraining.
Integrating a decision-making stage into radiology report generation can dramatically enhance diagnostic accuracy by resolving conflicting information without retraining models.
Automatic radiology report generation (RRG) aims to simulate the workflow of radiologists, assisting them in clinical diagnosis. However, existing methods often fall short in utilizing all information relevant to the examination, as is typically done in clinical practice. Although some works attempt to incorporate multi-view images and historical data, these additional inputs may sometimes lead to avoidable diagnostic errors on the contrary. To address these challenges, we introduce a decision-making stage after report generation for the first time and propose a Posterior Diagnostic Decision framework (PDD-RRG) to integrate potentially conflicting diagnoses. Specifically, we create various subsets of input data and utilize an existing RRG model to generate reports from different perspectives. Then the Bayesian posterior probability and the learned thresholds for each clinical observation are calculated to obtain an aggregated diagnostic conclusion, which is subsequently used to refine the generated report. Experiments on MIMIC-CXR demonstrate that our proposed PDD-RRG can effectively enhance the clinical efficacy of existing RRG models without any retraining.
