ArticleFrontiers in aging neuroscience2016
Discriminative Learning for Alzheimer's Disease Diagnosis via Canonical Correlation Analysis and Multimodal Fusion.
Article in Frontiers in aging neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 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
- Assessing cognitive impairment and disability in older adults through the lens of whole brain white matter patterns.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis.Cognitive neurodynamics · 2024Review
- A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data.Journal of medical signals and sensors · 2024Review
- Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response.Vaccines · 2023Article
- Hyper-graph based sparse canonical correlation analysis for the diagnosis of Alzheimer's disease from multi-dimensional genomic data.Methods (San Diego, Calif.) · 2021Article
- A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features.Frontiers in neuroinformatics · 2020Article
- Predicting Alzheimer's Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data.Frontiers in aging neuroscience · 2020Article
- Identifying Early Mild Cognitive Impairment by Multi-Modality MRI-Based Deep Learning.Frontiers in aging neuroscience · 2020Article
- Article
- Effective Diagnosis of Alzheimer's DiseaseFrontiers in genetics · 2019Article
- Fusion of deep learning models of MRI scans, Mini-Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment.Alzheimer's & dementia (Amsterdam, Netherlands) · 2018Article
- Multi-task fused sparse learning for mild cognitive impairment identification.Technology and health care : official journal of the European Society for Engineering and Medicine · 2018Article
- Joint regression and classification via relational regularization for Parkinson's disease diagnosis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2018Article
- Using Electronic Medical Record to Identify Patients With Dyslipidemia in Primary Care Settings: International Classification of Disease Code Matters From One Region to a National Database.Biomedical informatics insights · 2017Article
- Longitudinal Analysis for Disease Progression via Simultaneous Multi-Relational Temporal-Fused Learning.Frontiers in aging neuroscience · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To address the challenging task of diagnosing neurodegenerative brain disease, such as Alzheimer's disease (AD) and mild cognitive impairment (MCI), we propose a novel method using discriminative feature learning and canonical correlation analysis (CCA) in this paper. Specifically, multimodal features and their CCA projections are concatenated together to represent each subject, and hence both individual and shared information of AD disease are captured. A discriminative learning with multilayer feature hierarchy is designed to further improve performance. Also, hybrid representation is proposed to maximally explore data from multiple modalities. A novel normalization method is devised to tackle the intra- and inter-subject variations from the multimodal data. Based on our extensive experiments, our method achieves an accuracy of 96.93% [AD vs. normal control (NC)], 86.57 % (MCI vs. NC), and 82.75% [MCI converter (MCI-C) vs. MCI non-converter (MCI-NC)], respectively, which outperforms the state-of-the-art methods in the literature.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.