arxiv:2410.13085

MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models

Published on Oct 16

· Submitted by

richardxp888 on Oct 18

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Authors:

Peng Xia ,

Kangyu Zhu ,

Haoran Li ,

Huaxiu Yao

Abstract

Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in disease diagnosis and treatment planning. Recent progress in Medical Large Vision-Language Models (Med-LVLMs) has opened up new possibilities for interactive diagnostic tools. However, these models often suffer from factual hallucination, which can lead to incorrect diagnoses. Fine-tuning and retrieval-augmented generation (RAG) have emerged as methods to address these issues. However, the amount of high-quality data and distribution shifts between training data and deployment data limit the application of fine-tuning methods. Although RAG is lightweight and effective, existing RAG-based approaches are not sufficiently general to different medical domains and can potentially cause misalignment issues, both between modalities and between the model and the ground truth. In this paper, we propose a versatile multimodal RAG system, MMed-RAG, designed to enhance the factuality of Med-LVLMs. Our approach introduces a domain-aware retrieval mechanism, an adaptive retrieved contexts selection method, and a provable RAG-based preference fine-tuning strategy. These innovations make the RAG process sufficiently general and reliable, significantly improving alignment when introducing retrieved contexts. Experimental results across five medical datasets (involving radiology, ophthalmology, pathology) on medical VQA and report generation demonstrate that MMed-RAG can achieve an average improvement of 43.8% in the factual accuracy of Med-LVLMs. Our data and code are available in https://github.com/richard-peng-xia/MMed-RAG.

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richardxp888

Paper author Paper submitter 20 days ago

🚀 We introduce MMed-RAG, a powerful multimodal RAG system that boosts the factuality of Medical Vision-Language Models (Med-LVLMs) by up to 43.8%! 🩺💡

🔍 MMed-RAG enhances alignment across medical domains like radiology, pathology, and ophthalmology with a domain-aware retrieval mechanism. And it tackles three key challenges in alignment of multimodal RAG ✏:

1️⃣ Direct Copy Homework from Others❌ Think it by Self ✅
MMed-RAG helps Med-LVLMs avoid blindly copying external information by encouraging the model to rely on its own visual reasoning when solving complex problems. 🧠

2️⃣ Cannot Solve Problems by Self❌ Learn How to Copy ✅
When Med-LVLMs are unsure, MMed-RAG teaches the model to intelligently use retrieved knowledge, pulling in the right information at the right time, boosting accuracy, and reducing errors. 📚🔍

3️⃣ Copied Homework is Wrong❌ Avoid Interference from Incorrect Homework ✅
MMed-RAG prevents models from being misled by incorrect retrievals, reducing the risk of generating inaccurate medical diagnoses. 🚫🛑

📊 The results speak for themselves: a 43.8% improvement in factual accuracy across tasks like Medical VQA and report generation. This makes Med-LVLMs more reliable and trustworthy in critical healthcare applications! 🌟

MMed-RAG has the potential to be extended beyond healthcare, offering solutions for more general domains where factual accuracy and reliable retrieval are critical!🌍💡