Edge-Enhanced Dilated Residual Attention Network for Multimodal Medical Image Fusion

Multimodal medical image fusion is a crucial task that combines complementary information from different imaging modalities into a unified representation, thereby enhancing diagnostic accuracy and treatment planning. While deep learning methods, particularly Convolutional Neural Networks (CNNs) and Transformers, have significantly advanced fusion performance, some of the existing CNNbased methods fall short in capturing fine-grained multiscale and edge features, leading to suboptimal feature integration. Transformer-based models, on the other hand, are computationally intensive in both the training and fusion stages, making them impractical for real-time clinical use. Moreover, the clinical application of fused images remains unexplored. In this paper, we propose a novel CNN-based architecture that addresses these limitations by introducing a Dilated Residual Attention Network Module for effective multiscale feature extraction, coupled with a gradient operator to enhance edge detail learning. To ensure fast and efficient fusion, we present a parameter-free fusion strategy based on the weighted nuclear norm of softmax, which requires no additional computations during training or inference. Extensive experiments, including a downstream brain tumor classification task, demonstrate that our approach outperforms various baseline methods in terms of visual quality, texture preservation, and fusion speed, making it a possible practical solution for real-world clinical applications. Medical imaging plays an increasingly prominent role in clinical diagnosis, it aims to aggregate common and complementary information from different image modalities as well as integrate the information to generate more clearer images (Xie et al., 2023). Medical image fusion can enhance crucial details of anatomy and tissue information from different image modalities and hence helps physicians and radiologists in accurate diagnosis of diseases, e.g., precise localization of tumor boundaries and tissues (Chen et al., 2024) and effective radiotherapy treatments (Safari et al., 2023; Xie et al., 2023).