A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification
Published 2021 View Full Article
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Title
A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification
Authors
Keywords
Alzheimer's disease, Convolutional neural network, Attention mechanism, Early detection, Structural MRI
Journal
MAGNETIC RESONANCE IMAGING
Volume 78, Issue -, Pages 119-126
Publisher
Elsevier BV
Online
2021-02-15
DOI
10.1016/j.mri.2021.02.001
References
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Related references
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- (2018) Manhua Liu et al. NEUROINFORMATICS
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- Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
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- (2008) S. Kloppel et al. BRAIN
- Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls
- (2006) Jason P. Lerch et al. NEUROBIOLOGY OF AGING
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