Adaptive Clinical Risk Allocation for Radiology Report Generation

Clinically reliable radiology report generation requires models to identify and control clinically distinct errors. Despite recent progress, existing sequence-level methods typically combine multiple clinical discrepancies with fixed weights, leaving the optimization focus unchanged as the generator's error profile evolves during training. We propose ACRA, an adaptive clinical risk allocation framework that adjusts the optimization focus as the report generator evolves. ACRA maps generated and reference reports into a shared structured finding space and tracks the generator's greedy-decoding risk profile across five reference-conditioned discrepancy categories: omission, unsupported positive finding, polarity inconsistency, laterality error, and severity error. It then applies prior-regularized online mirror ascent to allocate greater optimization mass to persistent risks while preventing excessive concentration on any single category. The resulting allocation defines a risk-aware self-critical objective for comparing sampled and greedy reports. All risk-related components are used only during training and are removed at inference. Experiments on MIMIC-CXR and IU-Xray show that ACRA improves clinical consistency and reduces reference-conditioned clinical risk while maintaining competitive report quality. These results demonstrate that adapting optimization priorities to the evolving clinical error profile enables more effective risk control without adding inference-time modules.