Variational Finding-Anchored Direct Preference Optimization for Radiology Report Generation

Radiology Report Generation (RRG) aims to generate diagnostic reports from medical radiology images and has achieved substantial progress on benchmark datasets. Nonetheless, existing RRG methods may produce clinically inconsistent reports, with omitted findings and imperfect entity-relation representations. Direct Preference Optimization (DPO) and its variants offer a way to improve report generation through preference alignment. However, adapting DPO to finding‑specific preference alignment remains challenging. In this work, we propose a Variational Finding-Anchored Direct Preference Optimization (VF-DPO) approach that introduces finding-aware Question Answer pairs as latent variables to anchor the generation process. Specifically, we approximate the conditional report generation probability with finding anchors as conditions via its Evidence Lower BOund (ELBO), with which we derive a novel DPO formulation in a unified preference optimization objective that jointly models preference in latent finding-state alignment and high-fidelity report generation. To support this bi-level alignment, we construct a corresponding bi-level preference dataset comprising both finding-anchored latent variables and report sequences for training VF-DPO. At test time, we use the trained model to directly generate the radiology report, where the finding-anchored latent variables are implicitly accounted for in training, without being explicitly generated in the testing phase. Extensive experiments on MIMIC-CXR (X-ray) and CT-RATE (CT) demonstrate that VF-DPO achieves the highest F1 and RadGraph scores across all backbone comparisons, while obtaining the best RadCliQ and RaTEScore results on most backbones. These results indicate that VF-DPO enhances overall clinical consistency and outperforms state-of-the-art preference optimization methods for RRG.