Inspired by the success of large language models (LLMs), there is growing research interest in developing LLMs in the medical domain to assist clinicians. However, for hospitals, using closed-source commercial LLMs involves privacy issues, and developing open-source public LLMs requires large-scale computational resources, which are usually limited, especially in resource-efficient regions and low-income countries. We propose an open-source Small Language and Vision Assistant (SLaVA-CXR) that can be used for Chest X-Ray report automation. To efficiently train a small assistant, we first propose the Re$^3$Training method, which simulates the cognitive development of radiologists and optimizes the model in the Recognition, Reasoning, and Reporting training manner. Then, we introduce a data synthesis method, RADEX, which can generate a high-quality and diverse training corpus with privacy regulation compliance. The extensive experiments show that our SLaVA-CXR built on a 2.7B backbone not only outperforms but also achieves 6 times faster inference efficiency than previous state-of-the-art larger models.

本研究解决了在资源有限地区进行胸部X光报告自动化的隐私和计算资源问题。提出了Re$^3$Training方法，模仿放射科医生的认知发展，提高模型在识别、推理和报告方面的表现。同时，通过RADEX数据合成方法生成合规的高质量训练语料。实验结果表明，SLaVA-CXR在2.7B主干上构建，不仅超越了现有的大型模型，还实现了6倍的推理效率提升。

小型语言与视觉助手用于胸部X光报告自动化