SMFusion: Semantic-Preserving Fusion of Multimodal Medical Images for Enhanced Clinical Diagnosis

• URL: http://arxiv.org/abs/2505.12251v1
• Date: Sun, 18 May 2025 06:15:00 GMT
• Title: SMFusion: Semantic-Preserving Fusion of Multimodal Medical Images for Enhanced Clinical Diagnosis
• Authors: Haozhe Xiang, Han Zhang, Yu Cheng, Xiongwen Quan, Wanwan Huang,
• Abstract summary: We propose a novel semantic-guided medical image fusion approach that incorporates medical prior knowledge into the fusion process.<n>We generate diagnostic reports from the fused images to assess the preservation of medical information.<n> Experimental results on test datasets demonstrate that the proposed method achieves superior performance in both qualitative and quantitative evaluations.
• Score: 11.356721356096564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal medical image fusion plays a crucial role in medical diagnosis by integrating complementary information from different modalities to enhance image readability and clinical applicability. However, existing methods mainly follow computer vision standards for feature extraction and fusion strategy formulation, overlooking the rich semantic information inherent in medical images. To address this limitation, we propose a novel semantic-guided medical image fusion approach that, for the first time, incorporates medical prior knowledge into the fusion process. Specifically, we construct a publicly available multimodal medical image-text dataset, upon which text descriptions generated by BiomedGPT are encoded and semantically aligned with image features in a high-dimensional space via a semantic interaction alignment module. During this process, a cross attention based linear transformation automatically maps the relationship between textual and visual features to facilitate comprehensive learning. The aligned features are then embedded into a text-injection module for further feature-level fusion. Unlike traditional methods, we further generate diagnostic reports from the fused images to assess the preservation of medical information. Additionally, we design a medical semantic loss function to enhance the retention of textual cues from the source images. Experimental results on test datasets demonstrate that the proposed method achieves superior performance in both qualitative and quantitative evaluations while preserving more critical medical information.

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