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.

本文介绍了一种新颖的 LM-RRG 方法，它将大型模型与临床质量强化学习相结合，以生成准确和全面的胸部 X 射线放射学报告。通过设计驱动的大型语言模型特征提取器分析和解释胸部 X 射线图像的不同区域，强调具有医学意义的特定区域。基于大型模型的解码器，我们开发了一种多模态报告生成器，利用来自视觉特征和文本指导的多模态提示以自回归方式生成放射学报告。最后，为了更好地反映放射科医生通常在报告中分配的临床重要和不重要的错误，我们引入了一种新颖的临床质量强化学习策略，在学习过程中使用放射学报告临床质量（RadCliQ）指标作为奖励函数。在 MIMIC-CXR 和 IU-Xray 数据集上进行了大量实验，证明了我们方法的优越性。