This review sets out to discuss the foremost applications of artificial intelligence (AI),
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …

The explosive growth of biomedical Big Data presents both significant opportunities and
challenges in the realm of knowledge discovery and translational applications within …

Although many powerful convolutional neural networks (CNN) have been applied to various
image processing fields, due to the lack of datasets for network training and the significant …

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 …

Medical image fusion is a synthesis of visual information present in any number of medical
imaging inputs into a single fused image without any distortion or loss of detail. It enhances …

The main objective of image fusion for multimodal medical images is to retrieve valuable
information by combining multiple images obtained from various sources into a single image …

Radiogenomics is a field where medical images and genomic profiles are jointly analyzed to
answer critical clinical questions. Specifically, people want to identify non-invasive imaging …

Healthcare is considered as one of the most promising application areas for artificial
intelligence and analytics (AIA) just after the emergence of the latter. AI combined to …

Recently, image fusion has become one of the most promising fields in image processing
since it plays an essential role in different applications, such as medical diagnosis and …

The image fusion method based on deep learning has problems such as the supervised
learning of the model, the edge and noise of the fused image, and the setting of the image …