电子科技大学学报2026，Vol.55Issue(2)：275-288,14.DOI:10.12178/1001-0548.2024298
基于扩散的多模态医学图像融合
Diffusion-based multimodal medical image fusion
摘要
Abstract
In recent years,with the continuous development of medical imaging technology,image fusion techniques have been widely applied in medical image analysis.Traditional fusion methods are limited by manually designed feature extraction processes,resulting in limited accuracy in understanding and matching of image semantic information,and inability to fully utilize the information from multimodal images.A diffusion-based multimodal image fusion method is investigated in this paper.This method progressively learns the joint features of multi-channel images in the latent space using a diffusion model to overcome the limited learning capability of single end-to-end networks.And it generates high-quality fused images and improve the reverse denoising process specifically for the task of multimodal medical image fusion.Two modal discriminators are incorporated to enhance the denoising network's understanding of modality-specific features,fully leveraging the complementary information between different imaging modalities.Experiments on the AANLIB dataset demonstrate that the proposed method achieves satisfactory fusion results.关键词
深度学习/图像融合/扩散模型/多模态Key words
deep learning/image fusion/diffusion model/multimodal分类
信息技术与安全科学引用本文复制引用
罗佳,刘子枫,丁熠,卜君健,秦志光..基于扩散的多模态医学图像融合[J].电子科技大学学报,2026,55(2):275-288,14.基金项目
国家自然科学基金(62076054,62027827,62072074,62372083) （62076054,62027827,62072074,62372083）
四川省科技计划项目(2022JDJQ0039) （2022JDJQ0039）
