Inspired by the tremendous success of Large Language Models (LLMs), existing X-ray medical report generation methods attempt to leverage large models to achieve better performance. They usually adopt a Transformer to extract the visual features of a given X-ray image, and then, feed them into the LLM for text generation. How to extract more effective information for the LLMs to help them improve final results is an urgent problem that needs to be solved. Additionally, the use of visual Transformer models also brings high computational complexity. To address these issues, this paper proposes a novel context-guided efficient X-ray medical report generation framework. Specifically, we introduce the Mamba as the vision backbone with linear complexity, and the performance obtained is comparable to that of the strong Transformer model. More importantly, we perform context retrieval from the training set for samples within each mini-batch during the training phase, utilizing both positively and negatively related samples to enhance feature representation and discriminative learning. Subsequently, we feed the vision tokens, context information, and prompt statements to invoke the LLM for generating high-quality medical reports. Extensive experiments on three X-ray report generation datasets (i.e., IU-Xray, MIMIC-CXR, CheXpert Plus) fully validated the effectiveness of our proposed model. The source code of this work will be released on \url{https://github.com/Event-AHU/Medical_Image_Analysis}.

本研究解决了现有X射线医学报告生成方法在提取有效信息和计算复杂性方面的不足。提出了一种上下文引导的高效生成框架，通过从训练集中检索样本以增强特征表示能力，从而生成高质量的医学报告。实验结果表明，该方法在多个数据集上具有显著的性能提升。

R2GenCSR：基于大型语言模型的X射线医学报告生成的上下文样本检索