标题： $μ^2$分词器：用于放射学报告生成的可微分多尺度多模态分词器 显示英文标题

标题： $μ^2$Tokenizer: Differentiable Multi-Scale Multi-Modal Tokenizer for Radiology Report Generation

摘要： 自动化放射学报告生成（RRG）旨在从临床影像（如计算机断层扫描（CT）扫描）生成详细的文本报告，以提高诊断的准确性和效率以及管理建议的提供。 RRG受到两个关键挑战的困扰：（1）在资源有限的情况下从影像数据中提取相关信息的固有复杂性，以及（2）客观评估模型生成报告与专家撰写报告之间差异的困难。 为了解决这些挑战，我们提出了$\mu^2$LLM，一种$\underline{\textbf{mu}}$ltiscale$\underline{\textbf{mu}}$ltimodal 大型语言模型用于RRG任务。 新颖的${\mu}^2$分词器作为中间层，整合了多尺度视觉分词器和文本分词器的多模态特征，然后通过直接偏好优化（DPO）提高报告生成质量，由GREEN-RedLlama指导。 在四个大型CT图像-报告医学数据集上的实验结果表明，我们的方法优于现有方法，突显了我们在有限数据上微调的$\mu^2$LLM在RRG任务中的潜力。 同时，对于提示工程，我们引入了一个五阶段、LLM驱动的流程，将常规CT报告转换为成对的视觉-问题-答案三元组和引用链接的推理叙述，为可解释的多模态放射学LLM创建一个可扩展的高质量监督语料库。 所有代码、数据集和模型将在我们的官方仓库中公开。 https://github.com/Siyou-Li/u2Tokenizer 显示英文摘要

摘要： Automated radiology report generation (RRG) aims to produce detailed textual reports from clinical imaging, such as computed tomography (CT) scans, to improve the accuracy and efficiency of diagnosis and provision of management advice. RRG is complicated by two key challenges: (1) inherent complexity in extracting relevant information from imaging data under resource constraints, and (2) difficulty in objectively evaluating discrepancies between model-generated and expert-written reports. To address these challenges, we propose $\mu^2$LLM, a $\underline{\textbf{mu}}$ltiscale $\underline{\textbf{mu}}$ltimodal large language models for RRG tasks. The novel ${\mu}^2$Tokenizer, as an intermediate layer, integrates multi-modal features from the multiscale visual tokenizer and the text tokenizer, then enhances report generation quality through direct preference optimization (DPO), guided by GREEN-RedLlama. Experimental results on four large CT image-report medical datasets demonstrate that our method outperforms existing approaches, highlighting the potential of our fine-tuned $\mu^2$LLMs on limited data for RRG tasks. At the same time, for prompt engineering, we introduce a five-stage, LLM-driven pipeline that converts routine CT reports into paired visual-question-answer triples and citation-linked reasoning narratives, creating a scalable, high-quality supervisory corpus for explainable multimodal radiology LLM. All code, datasets, and models will be publicly available in our official repository. https://github.com/Siyou-Li/u2Tokenizer