🤖 AI Summary
This work addresses the challenge that existing medical image fusion methods struggle to simultaneously preserve global statistical similarity—such as correlation coefficient (CC) and mutual information (MI)—and local structural fidelity. To this end, we propose a reliability-weighted dual-expert fusion framework that integrates a spatial-domain expert for modeling global context and a wavelet-frequency-domain expert for capturing fine local details. The framework employs a dense reliability map to guide adaptive modality weighting and incorporates a soft gradient arbitration mechanism. Furthermore, it adopts a residual-mean fusion paradigm and a joint CC-MI loss function. Extensive experiments on CT-MRI, PET-MRI, and SPECT-MRI datasets demonstrate that our method significantly outperforms state-of-the-art approaches such as AdaFuse and ASFE-Fusion, achieving superior local structural preservation without compromising global statistical consistency.
📝 Abstract
Medical image fusion integrates complementary information from multiple imaging modalities to improve clinical interpretation. However, existing deep learningbased methods, including recent spatial-frequency frameworks such as AdaFuse and ASFE-Fusion, often suffer from a fundamental trade-off between global statistical similaritymeasured by correlation coefficient (CC) and mutual information (MI)and local structural fidelity. This paper proposes W-DUALMINE, a reliability-weighted dual-expert fusion framework designed to explicitly resolve this trade-off through architectural constraints and a theoretically grounded loss design. The proposed method introduces dense reliability maps for adaptive modality weighting, a dual-expert fusion strategy combining a global-context spatial expert and a wavelet-domain frequency expert, and a soft gradient-based arbitration mechanism. Furthermore, we employ a residual-to-average fusion paradigm that guarantees the preservation of global correlation while enhancing local details. Extensive experiments on CT-MRI, PET-MRI, and SPECT-MRI datasets demonstrate that W-DUALMINE consistently outperforms AdaFuse and ASFE-Fusion in CC and MI metrics while