MammoWise：多模型本地RAG流水线用于乳腺X线摄影报告生成
MammoWise: Multi-Model Local RAG Pipeline for Mammography Report Generation
AI总结：
MammoWise是一种本地多模型流水线，通过多任务分类和检索增强生成技术，实现乳腺X线摄影报告的高准确度生成与分类。
摘要：
乳腺筛查摄影具有高吞吐量、时间敏感性和文档密集性。放射科医生必须将细微的视觉发现转化为一致的BI-RADS评估、乳腺密度类别和结构化叙述报告。尽管最近的视觉语言模型（VLMs）能够实现图像到文本的报告生成，但许多依赖于封闭云系统或紧密耦合的架构，限制了隐私、可重复性和适应性。我们提出了MammoWise，一种本地多模型流水线，将开源VLMs转换为乳腺X线摄影报告生成器和多任务分类器。MammoWise支持任何Ollama托管的VLM和乳腺X线摄影数据集，并支持零样本、少量样本和思考链提示，可选地使用向量数据库进行多模态检索增强生成（RAG）以获取病例特定的上下文。我们评估了MedGemma、LLaVA-Med和Qwen2.5-VL在VinDr-Mammo和DMID数据集上的表现，评估报告质量（BERTScore、ROUGE-L）、BI-RADS分类、乳腺密度和关键发现。报告生成始终表现良好，随着少量样本提示和RAG的使用而提高。分类是可行的，但对模型和数据集的选择敏感。MedGemma的参数高效微调（QLoRA）提高了可靠性，实现了BI-RADS准确度0.7545、密度准确度0.8840和钙化准确度0.9341，同时保持报告质量。MammoWise提供了一个实用且可扩展的框架，用于在统一且可重复的流程中部署本地VLMs进行乳腺X线摄影报告生成。
英文摘要：
Screening mammography is high volume, time sensitive, and documentation heavy. Radiologists must translate subtle visual findings into consistent BI-RADS assessments, breast density categories, and structured narrative reports. While recent Vision Language Models (VLMs) enable image-to-text reporting, many rely on closed cloud systems or tightly coupled architectures that limit privacy, reproducibility, and adaptability. We present MammoWise, a local multi-model pipeline that transforms open source VLMs into mammogram report generators and multi-task classifiers. MammoWise supports any Ollama-hosted VLM and mammography dataset, and enables zero-shot, few-shot, and Chain-of-Thought prompting, with optional multimodal Retrieval Augmented Generation (RAG) using a vector database for case-specific context. We evaluate MedGemma, LLaVA-Med, and Qwen2.5-VL on VinDr-Mammo and DMID datasets, assessing report quality (BERTScore, ROUGE-L), BI-RADS classification, breast density, and key findings. Report generation is consistently strong and improves with few-shot prompting and RAG. Classification is feasible but sensitive to model and dataset choice. Parameter-efficient fine-tuning (QLoRA) of MedGemma improves reliability, achieving BI-RADS accuracy of 0.7545, density accuracy of 0.8840, and calcification accuracy of 0.9341 while preserving report quality. MammoWise provides a practical and extensible framework for deploying local VLMs for mammography reporting within a unified and reproducible workflow.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
