Computed Tomography Visual Question Answering with Cross-modal Feature Graphing

• URL: http://arxiv.org/abs/2507.04333v1
• Date: Sun, 06 Jul 2025 10:37:16 GMT
• Title: Computed Tomography Visual Question Answering with Cross-modal Feature Graphing
• Authors: Yuanhe Tian, Chen Su, Junwen Duan, Yan Song,
• Abstract summary: Visual question answering (VQA) in medical imaging aims to support clinical diagnosis by automatically interpreting complex imaging data in response to natural language queries.<n>Existing studies typically rely on distinct visual and textual encoders to independently extract features from medical images and clinical questions, which are subsequently combined to generate answers.<n>We propose a novel large language model (LLM)-based framework enhanced by a graph representation of salient features.
• Score: 16.269682136158004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual question answering (VQA) in medical imaging aims to support clinical diagnosis by automatically interpreting complex imaging data in response to natural language queries. Existing studies typically rely on distinct visual and textual encoders to independently extract features from medical images and clinical questions, which are subsequently combined to generate answers. Specifically, in computed tomography (CT), such approaches are similar to the conventional practices in medical image analysis. However, these approaches pay less attention to the spatial continuity and inter-slice correlations in the volumetric CT data, leading to fragmented and imprecise responses. In this paper, we propose a novel large language model (LLM)-based framework enhanced by a graph representation of salient features. Different from conventional multimodal encoding strategies, our approach constructs a cross-modal graph integrating both visual and textual features, treating individual CT slices and question tokens as nodes within the graph. We further leverage an attentive graph convolutional network to dynamically fuse information within this structure. The resulting aggregated graph features then serve as a soft prompt to guide a large language model in generating accurate answers. Extensive experiments on the M3D-VQA benchmark demonstrate that our approach consistently outperforms baselines across multiple evaluation metrics, offering more robust reasoning capabilities.

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