AMRG: Extend Vision Language Models for Automatic Mammography Report Generation

• URL: http://arxiv.org/abs/2508.09225v1
• Date: Tue, 12 Aug 2025 06:37:41 GMT
• Title: AMRG: Extend Vision Language Models for Automatic Mammography Report Generation
• Authors: Nak-Jun Sung, Donghyun Lee, Bo Hwa Choi, Chae Jung Park,
• Abstract summary: Mammography report generation is a critical yet underexplored task in medical AI.<n>We introduce AMRG, the first end-to-end framework for generating narrative mammography reports.<n>We train and evaluate AMRG on DMID, a publicly available dataset of paired high-resolution mammograms and diagnostic reports.
• Score: 4.366802575084445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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