ArticleIEEE transactions on bio-medical engineering2016
Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification.
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 50 papers, 2 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines 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
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled it
- TMBclaw: tumor clone-aware graph learning improves immunotherapy response prediction across heterogeneous cohorts.Briefings in bioinformatics · 2025Article
- IRMA: Machine learning-based harmonization ofEuropean journal of nuclear medicine and molecular imaging · 2025Article
- Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods.Behavior genetics · 2024Review
- Stable multivariate lesion symptom mapping.Aperture neuro · 2024Article
- Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders.NeuroImage. Clinical · 2024Article
- Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI.Frontiers in aging neuroscience · 2023Article
- Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study.IEEE transactions on medical imaging · 2022Article
- Combined Radiomics Model for Prediction of Hematoma Progression and Clinical Outcome of Cerebral Contusions in Traumatic Brain Injury.Neurocritical care · 2022Article
- A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer's Disease.Frontiers in aging neuroscience · 2022Article
- Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models.iScience · 2021Review
- A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas.Laboratory investigation; a journal of technical methods and pathology · 2021Article
- An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer's disease.Alzheimer's research & therapy · 2021Article
- Biomarker Extraction Based on Subspace Learning for the Prediction of Mild Cognitive Impairment Conversion.BioMed research international · 2021Article
- 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
- Multi-Order Brain Functional Connectivity Network-Based Machine Learning Method for Recognition of Delayed Neurocognitive Recovery in Older Adults Undergoing Non-cardiac Surgery.Frontiers in neuroscience · 2021Article
- High-Order Laplacian Regularized Low-Rank Representation for Multimodal Dementia Diagnosis.Frontiers in neuroscience · 2021Article
- Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI.IEEE transactions on pattern analysis and machine intelligence · 2020Article
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4 authors.
Funding
Abstract
The high feature-dimension and low sample-size problem is one of the major challenges in the study of computer-aided Alzheimer's disease (AD) diagnosis. To circumvent this problem, feature selection and subspace learning have been playing core roles in the literature. Generally, feature selection methods are preferable in clinical applications due to their ease for interpretation, but subspace learning methods can usually achieve more promising results. In this paper, we combine two different methodological approaches to discriminative feature selection in a unified framework. Specifically, we utilize two subspace learning methods, namely, linear discriminant analysis and locality preserving projection, which have proven their effectiveness in a variety of fields, to select class-discriminative and noise-resistant features. Unlike previous methods in neuroimaging studies that mostly focused on a binary classification, the proposed feature selection method is further applicable for multiclass classification in AD diagnosis. Extensive experiments on the Alzheimer's disease neuroimaging initiative dataset showed the effectiveness of the proposed method over other state-of-the-art methods.
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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.