Large Model driven Radiology Report Generation with Clinical Quality Reinforcement Learning

TL;DR

This paper presents LM-RRG, a novel radiology report generation approach that combines large models with clinical quality reinforcement learning to produce more accurate and clinically relevant reports from chest X-ray images.

Contribution

It introduces a large model driven feature extractor, a multimodal report generator, and a clinical quality reinforcement learning strategy using RadCliQ as a reward.

Findings

Outperforms state-of-the-art methods on MIMIC-CXR and IU-Xray datasets.

Achieves higher clinical relevance and accuracy in generated reports.

Demonstrates the effectiveness of reinforcement learning with RadCliQ in medical report generation.

Abstract

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.