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This research addresses factual inconsistencies in medical visual question answering by introducing a Gated Multimodal Unit to optimize retrieval within Retrieval-Augmented Generation frameworks. By learning joint query representations from both images and text, the method effectively leverages multimodal semantics while keeping the base language model frozen. The results demonstrate that this approach significantly improves evidence retrieval accuracy compared to single-modality baselines.
Enhancing RAG-based Medical VQA with Gated Multimodal Representations
arXiv:2610.23588v1 Announce Type: cross Abstract: Medical Vision-Language Models (Med-LVLMs) have shown strong performance in Medical Visual Question Answering (MVQA), yet they may still produce factually inconsistent responses. Retrieval-Augmented Generation (RAG) can mitigate this issue by incorporating external clinical evidence; however, retrieval is often guided by a single modality, potentially overlooking the joint semantics of medical images and clinical questions. We introduce a multimodal RAG framework that learns a joint query representation through a Gated Multimodal Unit, enabling evidence retrieval to leverage information from both modalities while keeping the underlying Med-LVLM frozen. We evaluate the proposed approach on benchmark datasets. Results show that the multimodal query representation consistently outperforms a reference Med-LVLM and remains competitive with state-of-the-art methods. Our findings demonstrate the potential of multimodal query representations for more effective evidence retrieval in Medical VQA.
Abstract:Medical Vision-Language Models (Med-LVLMs) have shown strong performance in Medical Visual Question Answering (MVQA), yet they may still produce factually inconsistent responses. Retrieval-Augmented Generation (RAG) can mitigate this issue by incorporating external clinical evidence; however, retrieval is often guided by a single modality, potentially overlooking the joint semantics of medical images and clinical questions. We introduce a multimodal RAG framework that learns a joint query representation through a Gated Multimodal Unit, enabling evidence retrieval to leverage information from both modalities while keeping the underlying Med-LVLM frozen. We evaluate the proposed approach on benchmark datasets. Results show that the multimodal query representation consistently outperforms a reference Med-LVLM and remains competitive with state-of-the-art methods. Our findings demonstrate the potential of multimodal query representations for more effective evidence retrieval in Medical VQA.
Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
arXiv:2610.23588 [cs.CV]
(or arXiv:2610.23588v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.23588 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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