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

arXiv: https://arxiv.org/abs/2411.11799 · HTML: https://mlsanity.com/paper/2411.11799/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Pyramid Attention Network for Semantic Segmentation](https://mlsanity.com/paper/1805.10180/) (2018)
- [Structure-Preserving Super Resolution with Gradient Guidance](https://mlsanity.com/paper/2003.13081/) (2020)
- [An Attention-based Multi-Scale Feature Learning Network for Multimodal Medical Image Fusion](https://mlsanity.com/paper/2212.04661/) (2022)

## Built on by (most cited first, 2 of 2)

- [TTTFusion: A Test-Time Training-Based Strategy for Multimodal Medical Image Fusion in Surgical Robots](https://mlsanity.com/paper/2504.20362/) (2025)
- [ClinicalFMamba: Advancing Clinical Assessment using Mamba-based Multimodal Neuroimaging Fusion](https://mlsanity.com/paper/2508.03008/) (2025)

## Abstract (arXiv)

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 CNN-based 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. The code will be released at https://github.com/simonZhou86/en_dran.