Enhancing Medical Visual Grounding via Knowledge-guided Spatial Prompts

• URL: http://arxiv.org/abs/2604.01915v1
• Date: Thu, 02 Apr 2026 11:31:30 GMT
• Title: Enhancing Medical Visual Grounding via Knowledge-guided Spatial Prompts
• Abstract summary: Medical Visual Grounding (MVG) aims to identify relevant phrases from free-text radiology reports and localize their corresponding regions in medical images.<n>We present KnowMVG, a knowledge-enhanced prompting strategy that encodes phrase related medical knowledge into compact embeddings.<n>This design bridges high-level semantic understanding and fine-grained visual perception without introducing extra textual reasoning overhead.
• Score: 52.256130375429414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical Visual Grounding (MVG) aims to identify diagnostically relevant phrases from free-text radiology reports and localize their corresponding regions in medical images, providing interpretable visual evidence to support clinical decision-making. Although recent Vision-Language Models (VLMs) exhibit promising multimodal reasoning ability, their grounding remains insufficient spatial precision, largely due to a lack of explicit localization priors when relying solely on latent embeddings. In this work, we analyze this limitation from an attention perspective and propose KnowMVG, a Knowledge-prior and global-local attention enhancement framework for MVG in VLMs that explicitly strengthens spatial awareness during decoding. Specifically, we present a knowledge-enhanced prompting strategy that encodes phrase related medical knowledge into compact embeddings, together with a global-local attention that jointly leverages coarse global information and refined local cues to guide precise region localization. localization. This design bridges high-level semantic understanding and fine-grained visual perception without introducing extra textual reasoning overhead. Extensive experiments on four MVG benchmarks demonstrate that our KnowMVG consistently outperforms existing approaches, achieving gains of 3.0% in AP50 and 2.6% in mIoU over prior state-of-the-art methods. Qualitative and ablation studies further validate the effectiveness of each component.

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