Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.
about
The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inceptionIndividual subject classification of mixed dementia from pure subcortical vascular dementia based on subcortical shape analysisPenalized likelihood phenotyping: unifying voxelwise analyses and multi-voxel pattern analyses in neuroimaging: penalized likelihood phenotypingEntorhinal Cortex Thickness across the Human LifespanAdvances in MRI biomarkers for the diagnosis of Alzheimer's disease.2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception.Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease.Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis.Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's DiseaseMulti-resolutional shape features via non-Euclidean wavelets: applications to statistical analysis of cortical thickness.A novel matrix-similarity based loss function for joint regression and classification in AD diagnosisPredicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers.Empowering imaging biomarkers of Alzheimer's diseaseBaseline shape diffeomorphometry patterns of subcortical and ventricular structures in predicting conversion of mild cognitive impairment to Alzheimer's disease.Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning.Clinical prediction from structural brain MRI scans: a large-scale empirical study.Neuroimaging-based methods for autism identification: a possible translational application?Predictive Models Based on Support Vector Machines: Whole-Brain versus Regional Analysis of Structural MRI in the Alzheimer's DiseaseMultimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism.Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer's disease: detecting, quantifying, and predicting.Domain Transfer Learning for MCI Conversion Prediction.Multimodal manifold-regularized transfer learning for MCI conversion prediction.Early Prediction of Alzheimer's Disease Using Null Longitudinal Model-Based Classifiers.The future of medical diagnostics: large digitized databases.A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages.Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosisSubspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification.A novel relational regularization feature selection method for joint regression and classification in AD diagnosisA semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer's diseaseScoring by nonlocal image patch estimator for early detection of Alzheimer's diseaseUsing Individualized Brain Network for Analyzing Structural Covariance of the Cerebral Cortex in Alzheimer's Patients.Locally linear embedding (LLE) for MRI based Alzheimer's disease classification.Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer's disease.Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features.Detection of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Longitudinal Brain MRI.Robust Identification of Alzheimer's Disease subtypes based on cortical atrophy patterns.Subcortical shape analysis of progressive mild cognitive impairment in Parkinson's disease.Alzheimer's Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning.Classification of Alzheimer's Disease and Prediction of Mild Cognitive Impairment Conversion Using Histogram-Based Analysis of Patient-Specific Anatomical Brain Connectivity Networks.Deep ensemble learning of sparse regression models for brain disease diagnosis.
P2860
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P2860
Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.
description
2011 nî lūn-bûn
@nan
2011 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Individual subject classificat ...... on of cortical thickness data.
@ast
Individual subject classificat ...... on of cortical thickness data.
@en
Individual subject classificat ...... on of cortical thickness data.
@nl
type
label
Individual subject classificat ...... on of cortical thickness data.
@ast
Individual subject classificat ...... on of cortical thickness data.
@en
Individual subject classificat ...... on of cortical thickness data.
@nl
prefLabel
Individual subject classificat ...... on of cortical thickness data.
@ast
Individual subject classificat ...... on of cortical thickness data.
@en
Individual subject classificat ...... on of cortical thickness data.
@nl
P2093
P2860
P1433
P1476
Individual subject classificat ...... on of cortical thickness data.
@en
P2093
Joon-Kyung Seong
Sung Yong Shin
Youngsang Cho
P2860
P304
P356
10.1016/J.NEUROIMAGE.2011.09.085
P407
P50
P577
2011-10-08T00:00:00Z