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Updated: Mar 21, 2026

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MRCE-Net: A multi-role collaborative experts deep learning network for multi-modal medical image fusion.

Pengwei Dong1, Bo Su1, Zhouxian Lu1

  • 1School of Remote Sensing Information Engineering, Wuhan University, Wuhan 430079, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|March 19, 2026
PubMed
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A novel Multi-Role Collaborative Experts Network (MRCE-Net) enhances multi-modal medical image fusion by balancing local and global features. This deep learning approach improves fusion accuracy and visual quality for better medical diagnoses.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Multi-modal medical image fusion combines complementary strengths of different imaging techniques.
  • Deep learning methods dominate medical image fusion, surpassing traditional approaches.
  • Existing methods face challenges in balancing local feature extraction and global context representation, and capturing modality-specific details.

Purpose of the Study:

  • To introduce the Multi-Role Collaborative Experts Network (MRCE-Net) for advanced multi-modal medical image fusion.
  • To address limitations in current fusion techniques regarding local-global feature balance and modality complementarity.
  • To enhance the accuracy and visual quality of fused medical images.

Main Methods:

  • Developed a dual-branch encoder utilizing window-based and global channel-based Transformers for local and global feature extraction, respectively.
Keywords:
CollaborativeMixture-of-expertsMulti-modal medical image fusionTransformer

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  • Proposed a Multi-Role Collaborative Experts fusion module to model distinct aspects of multi-modal features, emphasizing modality specificity and inter-modality complementarity.
  • Integrated specialized experts to jointly process features, aiming for comprehensive representation and accurate fusion.

    • Main Results:

    • The MRCE-Net demonstrated superior performance compared to state-of-the-art methods on a public benchmark and an in-house brain imaging dataset.
    • Achieved significant improvements in both visual quality and quantitative metrics for multi-modal medical image fusion.
    • The proposed network effectively balances local feature extraction with global context representation.

    Conclusions:

    • MRCE-Net offers a robust framework for multi-modal medical image fusion, outperforming existing techniques.
    • The method successfully captures modality-specific characteristics and inter-modality complementarity for enhanced fusion results.
    • The findings suggest MRCE-Net's potential to advance diagnostic capabilities through improved medical image analysis.