Large Model driven Radiology Report Generation with Clinical Quality Reinforcement Learning
This addresses the workload reduction for radiologists by improving report generation against clinical standards, though it appears incremental as it builds on existing large model and reinforcement learning techniques.
The paper tackles the problem of generating clinically accurate radiology reports from chest X-ray images by introducing LM-RRG, a method that integrates large models with clinical quality reinforcement learning, achieving state-of-the-art results on MIMIC-CXR and IU-Xray datasets.
Radiology report generation (RRG) has attracted significant attention due to its potential to reduce the workload of radiologists. Current RRG approaches are still unsatisfactory against clinical standards. This paper introduces a novel RRG method, \textbf{LM-RRG}, that integrates large models (LMs) with clinical quality reinforcement learning to generate accurate and comprehensive chest X-ray radiology reports. Our method first designs a large language model driven feature extractor to analyze and interpret different regions of the chest X-ray image, emphasizing specific regions with medical significance. Next, based on the large model's decoder, we develop a multimodal report generator that leverages multimodal prompts from visual features and textual instruction to produce the radiology report in an auto-regressive way. Finally, to better reflect the clinical significant and insignificant errors that radiologists would normally assign in the report, we introduce a novel clinical quality reinforcement learning strategy. It utilizes the radiology report clinical quality (RadCliQ) metric as a reward function in the learning process. Extensive experiments on the MIMIC-CXR and IU-Xray datasets demonstrate the superiority of our method over the state of the art.