标题： AMRG：扩展视觉语言模型以用于自动乳腺X线摄影报告生成 显示英文标题

标题： AMRG: Extend Vision Language Models for Automatic Mammography Report Generation

摘要： 乳腺摄影报告生成是医疗人工智能中一个关键但研究不足的任务，其特点包括多视图图像推理、高分辨率视觉线索和非结构化的放射学语言。 在本工作中，我们引入了AMRG（自动乳腺摄影报告生成），这是首个使用大型视觉-语言模型（VLMs）生成叙述性乳腺摄影报告的端到端框架。 基于MedGemma-4B-it这一领域专业化、指令调优的视觉-语言模型，我们通过低秩适应（LoRA）采用了一种参数高效的微调（PEFT）策略，实现了计算开销最小的轻量级适应。 我们在DMID上训练和评估AMRG，这是一个公开可用的配对高分辨率乳腺X光片和诊断报告的数据集。 这项工作建立了乳腺摄影报告生成的第一个可重复基准，解决了多模态临床人工智能中的长期空白。 我们系统地探索了LoRA超参数配置，并在多个VLM主干模型上进行了比较实验，包括在统一调优协议下的领域特定和通用模型。 我们的框架在语言生成和临床指标方面表现出色，达到了ROUGE-L得分为0.5691，METEOR为0.6152，CIDEr为0.5818，BI-RADS准确率为0.5582。 定性分析进一步突显了诊断一致性的提高和幻觉的减少。 AMRG为放射学报告生成提供了一个可扩展和可适应的基础，并为多模态医疗人工智能的未来研究铺平了道路。 显示英文摘要

摘要： Mammography report generation is a critical yet underexplored task in medical AI, characterized by challenges such as multiview image reasoning, high-resolution visual cues, and unstructured radiologic language. In this work, we introduce AMRG (Automatic Mammography Report Generation), the first end-to-end framework for generating narrative mammography reports using large vision-language models (VLMs). Building upon MedGemma-4B-it-a domain-specialized, instruction-tuned VLM-we employ a parameter-efficient fine-tuning (PEFT) strategy via Low-Rank Adaptation (LoRA), enabling lightweight adaptation with minimal computational overhead. We train and evaluate AMRG on DMID, a publicly available dataset of paired high-resolution mammograms and diagnostic reports. This work establishes the first reproducible benchmark for mammography report generation, addressing a longstanding gap in multimodal clinical AI. We systematically explore LoRA hyperparameter configurations and conduct comparative experiments across multiple VLM backbones, including both domain-specific and general-purpose models under a unified tuning protocol. Our framework demonstrates strong performance across both language generation and clinical metrics, achieving a ROUGE-L score of 0.5691, METEOR of 0.6152, CIDEr of 0.5818, and BI-RADS accuracy of 0.5582. Qualitative analysis further highlights improved diagnostic consistency and reduced hallucinations. AMRG offers a scalable and adaptable foundation for radiology report generation and paves the way for future research in multimodal medical AI.