ArticleIEEE transactions on bio-medical engineering2016
Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis.
Article in IEEE transactions on bio-medical engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.Brain informatics · 2025Review
- Optimizing a 3D convolutional neural network to detect Alzheimer's disease based on MRI.PeerJ. Computer science · 2025Article
- Toward accurate Alzheimer's detection: transfer learning with ResNet50 for MRI-based diagnosis.Frontiers in neuroscience · 2025Article
- Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation.Medical & biological engineering & computing · 2023Article
- Brain MRI Analysis for Alzheimer's Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- A Multiview Deep Learning Method for Brain Functional Connectivity Classification.Computational intelligence and neuroscience · 2022Article
- WTD-PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time-Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer's Disease.Computational intelligence and neuroscience · 2022Article
- Large Margin and Local Structure Preservation Sparse Representation Classifier for Alzheimer's Magnetic Resonance Imaging Classification.Frontiers in aging neuroscience · 2022Article
- Combined Multi-Atlas and Multi-Layer Perception for Alzheimer's Disease Classification.Frontiers in aging neuroscience · 2022Article
- Discovery of Genetic Biomarkers for Alzheimer's Disease Using Adaptive Convolutional Neural Networks Ensemble and Genome-Wide Association Studies.Interdisciplinary sciences, computational life sciences · 2021Article
- Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques.Journal of Alzheimer's disease reports · 2021Article
- Biomarker Identification Through Integrating fMRI and Epigenetics.IEEE transactions on bio-medical engineering · 2020Article
- A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging.Journal of neuroscience methods · 2020Article
- Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation.IEEE transactions on medical imaging · 2020Article
- Hierarchical Structured Sparse Learning for Schizophrenia Identification.Neuroinformatics · 2020Article
- A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks.Journal of medical systems · 2019Article
- Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises.IEEE transactions on pattern analysis and machine intelligence · 2019Article
- Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model.IEEE transactions on bio-medical engineering · 2019Article
- Neuroimaging modality fusion in Alzheimer's classification using convolutional neural networks.PloS one · 2019Article
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4 authors.
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Abstract
Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods.
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