ArticleBrain imaging and behavior2016
Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis.
Article in Brain imaging and behavior, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 109 citations in OpenAlex.
- A technical review of canonical correlation analysis for neuroscience applications.Human brain mapping · 2020Pooled it
- A Novel Hybrid Ordinal Learning Model with Health Care Application.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- [A multi-modal feature fusion classification model based on distance matching and discriminative representation learning for differentiation of high-grade glioma from solitary brain metastasis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- A unique color-coded visualization system with multimodal information fusion and deep learning in a longitudinal study of Alzheimer's disease.Artificial intelligence in medicine · 2023Article
- OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data.Brain sciences · 2023Article
- Performance comparison of bio-inspired and learning-based clustering analysis with machine learning techniques for classification of EEG signals.Frontiers in artificial intelligence · 2023Article
- A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer's Disease.Frontiers in aging neuroscience · 2022Article
- PET Imaging of Tau Pathology and Amyloid-β, and MRI for Alzheimer's Disease Feature Fusion and Multimodal Classification.Journal of Alzheimer's disease : JAD · 2021Article
- High-Order Laplacian Regularized Low-Rank Representation for Multimodal Dementia Diagnosis.Frontiers in neuroscience · 2021Article
- Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data.Medical image analysis · 2020Article
- Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing.Computational intelligence and neuroscience · 2020Article
- Structured sparsity regularized multiple kernel learning for Alzheimer's disease diagnosis.Pattern recognition · 2019Article
- Discriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis.Brain imaging and behavior · 2019Article
- Joint Pairing and Structured Mapping of Convolutional Brain Morphological Multiplexes for Early Dementia Diagnosis.Brain connectivity · 2019Article
- Robust multi-label transfer feature learning for early diagnosis of Alzheimer's disease.Brain imaging and behavior · 2019Article
- Multi-modal Neuroimaging Data Fusion via Latent Space Learning for Alzheimer's Disease Diagnosis.Predictive Intelligence in Medicine. PRIME (Workshop) · 2018Article
- Multi-Layer Multi-View Classification for Alzheimer's Disease Diagnosis.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2018Article
- Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder.Brain imaging and behavior · 2017Article
- A novel relational regularization feature selection method for joint regression and classification in AD diagnosis.Medical image analysis · 2017Article
- Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2017Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Fusing information from different imaging modalities is crucial for more accurate identification of the brain state because imaging data of different modalities can provide complementary perspectives on the complex nature of brain disorders. However, most existing fusion methods often extract features independently from each modality, and then simply concatenate them into a long vector for classification, without appropriate consideration of the correlation among modalities. In this paper, we propose a novel method to transform the original features from different modalities to a common space, where the transformed features become comparable and easy to find their relation, by canonical correlation analysis. We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer. In our experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we use Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multi-class disease status for Alzheimer's disease diagnosis. The experimental results showed that the proposed canonical feature selection method helped enhance the performance of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.
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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.