Radiology report generation is crucial in medical imaging,but the manual annotation process by physicians is time-consuming and labor-intensive, necessitating the develop-ment of automatic report generation methods. Existingresearch predominantly utilizes Transformers to generateradiology reports, which can be computationally intensive,limiting their use in real applications. In this work, we presentR2Gen-Mamba, a novel automatic radiology report genera-tion method that leverages the efficient sequence processingof the Mamba with the contextual benefits of Transformerarchitectures. Due to lower computational complexity ofMamba, R2Gen-Mamba not only enhances training and in-ference efficiency but also produces high-quality reports.Experimental results on two benchmark datasets with morethan 210,000 X-ray image-report pairs demonstrate the ef-fectiveness of R2Gen-Mamba regarding report quality andcomputational efficiency compared with several state-of-the-art methods. The source code can be accessed online.

本研究针对手动注释放射学报告耗时且劳动强度大的问题，提出了一种新颖的自动报告生成方法R2Gen-Mamba。该方法结合了Mamba的高效序列处理和Transformer的上下文优势，显著提升了训练和推理的效率，同时生成高质量报告。实验结果表明，R2Gen-Mamba在报告质量和计算效率方面超过了多种先进方法。

R2Gen-Mamba：一种选择性状态空间模型用于放射学报告生成