Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation
By Yudong Zhang, Zhengchao Dong, Shui-hua Wang, Xiang Yu, Xujing Yao, Qinghua Zhou, Hua Hu, Min Li, C. Jiménez-Mesa, J. Ramírez and 2 more. Information Fusion, 2020
392 citations recorded by the source corpus.
Highlights • We analysed over 450 references from all well-famed databases.• We provided a comprehensive survey on multimodal data fusion in neuroimaging.• This review encompassed current challenges & applications, strengths &limitations.• Fundamental fusion rules, and fusion quality assessment methods were reviewed.• Atlas-based fusion segmentation, quantification, & applications were reviewed.
Subfields: Biomedical Engineering, Cognitive Neuroscience, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/c672fe43b354a25608b78bf4085c49100bbde6e0
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In Biomedical Engineering
In Cognitive Neuroscience
In Computer Vision and Pattern Recognition
Citations in the corpus
- Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network (2020, background): “The existence of ICS will worsen the CNN’s performance [34,35].”
- Review of Multimodal Data Acquisition Approaches for Brain–Computer Interfaces (2024, unknown): “This limits the ability to study long-term neural dynamics, such as those involved in learning and memory, using the multimodal approach [88].”