# Long-range correlation-guided dual-encoder fusion network for medical images

## TL;DR

This paper introduces a new neural network for fusing medical images from different sources, improving the accuracy of combined images used in clinical settings.

## Contribution

The novel approach combines cross-dimension multi-scale feature extraction with long-range correlation fusion to enhance multimodal medical image fusion.

## Key findings

- The proposed model improves fusion metrics on lung medical images by up to 6.62%.
- On brain medical images, the model achieves a 15.71% improvement in one key fusion metric.
- The model effectively captures long-range dependencies between different image modalities.

## Abstract

Multimodal medical image fusion plays an important role in clinical applications. However, multimodal medical image fusion methods ignore the feature dependence among modals, and the feature fusion ability with different granularity is not strong. A Long-Range Correlation-Guided Dual-Encoder Fusion Network for Medical Images is proposed in this paper. The main innovations of this paper are as follows: Firstly, A Cross-dimension Multi-scale Feature Extraction Module (CMFEM) is designed in the encoder, by extracting multi-scale features and aggregating coarse-to-fine features, the model realizes fine-grained feature enhancement in different modalities. Secondly, a Long-range Correlation Fusion Module (LCFM) is designed, by calculating the long-range correlation coefficient between local features and global features, the same granularity features are fused by the long-range correlation fusion module. long-range dependencies between modalities are captured by the model, and different granularity features are aggregated. Finally, this paper is validated on clinical multimodal lung medical image dataset and brain medical data dataset. On the lung medical image dataset, IE, AG, \documentclass[12pt]{minimal}
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\begin{document}$${\textbf {Q}}^{{\textbf {AB/F}}}$$\end{document}, and EI metrics are improved by 4.53%, 4.10%, 6.19%, and 6.62% respectively. On the brain medical image dataset, SF, VIF, and \documentclass[12pt]{minimal}
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\begin{document}$${\textbf {Q}}^{{\textbf {AB/F}}}$$\end{document} metrics are improved by 3.88%, 15.71%, and 7.99% respectively. This model realizes better fusion performance, which plays an important role in the fusion of multimodal medical images.