Efficient self-attention mechanism and structural distilling model for Alzheimer's disease diagnosis

Efficient self-attention mechanism and structural distilling model for Alzheimer's disease diagnosis

Structural magnetic resonance imaging (sMRI) is commonly used for the identification of Alzheimer's disease because of its keen insight into atrophy-induced changes in brain structure. Current mainstream convolutional neural network-based deep learning methods ignore the long-term dependencies...

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Bibliographic Details
Main Authors: Fan, X. (Author), Gong, J. (Author), He, H. (Author), Liang, P. (Author), Lin, R. (Author), Miao, J. (Author), Tan, Y. (Author), Zhu, J. (Author), Zhu, Y. (Author)
Format: Article
Language:English
Published: Elsevier Ltd 2022
Subjects:
Alzheimers disease
Alzheimer's disease
Attention mechanisms
Brain structure
Classification
Classification (of information)
Complex networks
Computational complexity
Computer aided diagnosis
Convolutional neural network
Convolutional neural networks
Cost reduction
'current
Deep learning
Disease diagnosis
Feature distilling
Learning methods
Learning systems
Magnetic resonance imaging
Network-based
Neurodegenerative diseases
Neuroimaging
Self-attention mechanism
Vector spaces
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