Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data.
about
Genetics of the connectomeHierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.Bi-level multi-source learning for heterogeneous block-wise missing data.Understanding cognitive deficits in Alzheimer's disease based on neuroimaging findingsSparse Methods for Biomedical DataAnalysis of sampling techniques for imbalanced data: An n = 648 ADNI study.Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion.Whole-genome analyses of whole-brain data: working within an expanded search space2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception.View-aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multi-modality data.Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.Latent feature representation with stacked auto-encoder for AD/MCI diagnosisRecent advances in imaging Alzheimer's disease.Identifying the neuroanatomical basis of cognitive impairment in Alzheimer's disease by correlation- and nonlinearity-aware sparse Bayesian learning.Subclass-based multi-task learning for Alzheimer's disease diagnosisEmpowering imaging biomarkers of Alzheimer's diseaseDeep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis.Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment.Seemingly unrelated regression empowers detection of network failure in dementia.Manifold regularized multitask feature learning for multimodality disease classificationPrognostic classification of mild cognitive impairment and Alzheimer's disease: MRI independent component analysisSurface fluid registration of conformal representation: application to detect disease burden and genetic influence on hippocampusAccurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairmentMaximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features.Integrative biomarker discovery in neurodegenerative diseases.Multi-Hypergraph Learning for Incomplete Multi-Modality Data.Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson's Disease.Extracting patterns of morphometry distinguishing HIV associated neurodegeneration from mild cognitive impairment via group cardinality constrained classification.Learning-based subject-specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies.Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment.Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis.Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease.Harnessing the informatics revolution for neuroscience drug R&D.
P2860
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P2860
Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data.
description
2012 nî lūn-bûn
@nan
2012 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի մարտին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
Multi-source feature learning ...... terogeneous neuroimaging data.
@ast
Multi-source feature learning ...... terogeneous neuroimaging data.
@en
Multi-source feature learning ...... terogeneous neuroimaging data.
@nl
type
label
Multi-source feature learning ...... terogeneous neuroimaging data.
@ast
Multi-source feature learning ...... terogeneous neuroimaging data.
@en
Multi-source feature learning ...... terogeneous neuroimaging data.
@nl
prefLabel
Multi-source feature learning ...... terogeneous neuroimaging data.
@ast
Multi-source feature learning ...... terogeneous neuroimaging data.
@en
Multi-source feature learning ...... terogeneous neuroimaging data.
@nl
P2093
P2860
P1433
P1476
Multi-source feature learning ...... terogeneous neuroimaging data.
@en
P2093
Jieping Ye
Vaibhav A Narayan
Yalin Wang
P2860
P304
P356
10.1016/J.NEUROIMAGE.2012.03.059
P407
P577
2012-03-29T00:00:00Z