ReCER: Recurrent Temporal Relation Refinement for Longitudinal Radiology Report Generation

Longitudinal radiology report generation (LRRG) aims to describe both current findings and their temporal evolution relative to prior examinations. When subsequent finding-specific interpretation relies only on a fixed temporal summary, local cues underrepresented during initial aggregation may remain difficult to incorporate. This motivates refining the interpretation of each prior finding against retained local current–prior comparisons and using the resulting representation to guide both temporal description and historical reuse. To this end, we propose Recurrent Experts for Controlled Evidence Reuse (ReCER), a relation-refinement framework that preserves local current–prior comparison evidence and adaptively refines finding-specific temporal representations, enabling historical reuse to draw on local cues underrepresented in fixed summaries. For each previously reported positive finding, prior-report semantics guide the reading of current–prior visual relations, grounding its reinterpretation in evidence from both examinations. Recurrent relation experts then refine finding-specific temporal representations through state-guided attention, allowing each updated state to guide subsequent evidence aggregation. The refined representations are used to characterize temporal changes, construct temporal content, and derive finding-specific usage scores that control this content's contribution during report generation. ReCER also preserves an independent current-image pathway and accounts for newly emerging findings through a separate branch, so that report generation is not restricted to previously reported positive findings. Experiments on MIMIC-CXR, CheXpert Plus, and IU X-Ray demonstrate the effectiveness of ReCER across longitudinal and current-image-only settings. Our code is available at the anonymous link https://anonymous.4open.science/r/ReCERcode-6DB5/.