Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis

Keyphrases

• Mild Cognitive Impairment 100%
• Alzheimer's Disease 100%
• Hierarchical Feature Representation 100%
• Deep Boltzmann Machine 100%
• Deep Learning 100%
• Representation Fusion 100%
• Multimodal Fusion 100%
• Magnetic Resonance Imaging 80%
• Positron Emission Tomography 80%
• Feature Representation 40%
• Alzheimer's Disease Diagnosis 20%
• State-of-the-art Techniques 20%
• Systematic Method 20%
• ADNI Dataset 20%
• Diagnostic Accuracy 20%
• Healthy Normal 20%
• Neuroimaging 20%
• Cortical Thickness 20%
• Visual Inspection 20%
• Kernel Space 20%
• Deep Network 20%
• Binary Classification 20%
• Prodromal Stage 20%
• Feature Fusion 20%
• Deformation-based Morphometry 20%
• Gray Matter Volume 20%
• Neuroimaging Modalities 20%
• Long Vector 20%
• Joint Feature 20%
• Training Model 20%
• Handcrafted Features 20%
• Latent Feature Representation 20%
• Multimodal Features 20%
• Shared Feature Representation 20%
• 3D Patches 20%
• Restricted Boltzmann Machine 20%
• Multimodal Information 20%
• High-dimensional Kernel 20%
• Latent Patterns 20%

Computer Science

• Deep Boltzmann Machine 100%
• Deep Learning Method 100%
• Classification Problem 20%
• Building-Blocks 20%
• Cortical Thickness 20%
• Visual Inspection 20%
• Based Morphometry 20%
• Binary Classification 20%
• Diagnostic Accuracy 20%
• Dimensional Kernel 20%
• Boltzmann Machine 20%
• Multimodal Information 20%

Neuroscience

• Mild Cognitive Impairment 100%
• Alzheimer's Disease 100%
• Magnetic Resonance Imaging 80%
• Positron Emission Tomography 80%
• Gray Matter 20%