X Yang, K Huang, D Yang, W Zhao… - Global Challenges, 2024 - Wiley Online Library
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 …
T Tirupal, BC Mohan, SS Kumar - Current Signal Transduction …, 2021 - ingentaconnect.com
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 …
Q Liu, P Hu - Current opinion in chemical biology, 2022 - Elsevier
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 …
MA Saleh, AEA Ali, K Ahmed, AM Sarhan - Electronics, 2022 - mdpi.com
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 …
FP An, X Ma, L Bai - Biomedical Signal Processing and Control, 2022 - Elsevier
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 …