Multi-Image Support refers to the capability of a system or application to handle, display, or process multiple images simultaneously. This feature is commonly used in fields like medical imaging, digital photography, and software applications that require the comparison or analysis of several images at once. Multi-image support enables users to view and interact with various images side by side, combine them for advanced processing (e.g., image stitching or multi-frame analysis), or work with images from different sources. It improves efficiency and accuracy, especially in applications like diagnostics, where comparing multiple scans or perspectives is essential.

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Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation

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Multimodal Large Language Models (MLLMs) have shown impressive performance in vision and text tasks. However, hallucination remains a major challenge, especially in fields like healthcare where details are critical. In this work, we show how MLLMs may be enhanced to support Visual RAG (V-RAG), a retrieval-augmented generation framework that incorporates both text and visual data from retrieved images. On the MIMIC-CXR chest X-ray report generation and Multicare medical image caption generation datasets, we show that Visual RAG improves the accuracy of entity probing, which asks whether a medical entities is grounded by an image. We show that the improvements extend both to frequent and rare entities, the latter of which may have less positive training data. Downstream, we apply V-RAG with entity probing to correct hallucinations and generate more clinically accurate X-ray reports, obtaining a higher RadGraph-F1 score.

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