Purpose This study proposed an end-to-end unsupervised medical fusion generative
adversarial network, MedFusionGAN, to fuse computed tomography (CT) and high …

Image fusion techniques have been widely used for multi-modal medical image fusion tasks.
Most existing methods aim to improve the overall quality of the fused image and do not focus …

In this paper, a novel end-to-end model for fusing medical images characterizing structural
information, ie, IS, and images characterizing functional information, ie, IF, of different …

The fusion of multimodality medical images combines the most relevant information of the
source modalities to improve diagnostic accuracy. Most recently, end-to-end deep learning …

In recent years, extensive multimodal medical image fusion algorithms have been proposed.
However, existing methods are primarily based on specific transformation theories. There …

In this paper, we propose the medical Wasserstein generative adversarial networks
(MWGAN), an end-to-end model, for fusing magnetic resonance imaging (MRI) and positron …

In this paper, we propose a novel end-to-end model for fusing medical images
characterizing structural information, ie, IS, and images characterizing functional information …

CT and MRI image fusion is a popular research field that plays a vital role in clinical
diagnosis. To retain more salient features and complementary information from source …

Dear Editor, In recent years, multi-modal medical image fusion has received widespread
attention in the image processing community. However, existing works on medical image …

Multimodal medical image fusion (MMIF) is highly significant in such fields as disease
diagnosis and treatment. The traditional MMIF methods are difficult to provide satisfactory …