Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database.
about
Generative-discriminative basis learning for medical imagingImpact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014Imaging markers for Alzheimer disease: which vs howThe 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 analysisAn evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's diseaseRobust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data3D scattering transforms for disease classification in neuroimaging.Why musical memory can be preserved in advanced Alzheimer's disease.A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging.Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.Classification of self-driven mental tasks from whole-brain activity patternsCombined analysis of sMRI and fMRI imaging data provides accurate disease markers for hearing impairment.The utility of data-driven feature selection: re: Chu et al. 2012.Prediction of conversion from mild cognitive impairment to Alzheimer disease based on bayesian data mining with ensemble learning.Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study.Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion.PROBABILISTIC PREDICTION OF NEUROLOGICAL DISORDERS WITH A STATISTICAL ASSESSMENT OF NEUROIMAGING DATA MODALITIES.Spatial component analysis of MRI data for Alzheimer's disease diagnosis: a Bayesian network approach.Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data.Content-based image retrieval for brain MRI: an image-searching engine and population-based analysis to utilize past clinical data for future diagnosis.Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP DataEvaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data.2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception.Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease.View-aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multi-modality data.Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis.Development of a brain MRI-based hidden Markov model for dementia recognition.Brain region's relative proximity as marker for Alzheimer's disease based on structural MRI.Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's DiseaseLongitudinal deformation models, spatial regularizations and learning strategies to quantify Alzheimer's disease progression.Identifying informative imaging biomarkers via tree structured sparse learning for AD diagnosisFrequent and discriminative subnetwork mining for mild cognitive impairment classification.Statistical analysis of relative pose information of subcortical nuclei: application on ADNI data.Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting.Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review.Application of a MRI based index to longitudinal atrophy change in Alzheimer disease, mild cognitive impairment and healthy older individuals in the AddNeuroMed cohort.Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictorsHierarchical anatomical brain networks for MCI prediction: revisiting volumetric measures
P2860
Q24630139-00BC4018-C26F-4799-B1F8-A0258F2DEF3FQ26801629-9B0D01EE-5BD0-4BEF-B9E8-3CEEB88E6026Q27001254-C9DFD9C4-623E-49FB-9225-CE38212132A1Q27006831-5B47019C-D9B3-4E4B-B88A-A59C292FD30BQ28534320-08F89386-1C6F-4686-93A5-097920B98A1FQ28651736-1C3D45D2-D089-4FCE-9A18-E858BC11BCEFQ28683731-4E763E7F-AFE9-4388-BF9A-4301B7795732Q30361248-FA919930-79F8-4D1F-A4E4-A52A435417A7Q30375294-828B5E18-84D0-4C71-AA5C-0603A54169C8Q30393241-487875E6-4D45-4C82-A566-A08ED1857738Q30399994-F5D99E1C-3A35-4CF5-8974-6F4D7BC2D898Q30438428-430DF2AD-F027-41BB-9D88-13D5A2E8B311Q30445589-518DEA2A-A684-4D4C-AF2D-592E093D3E93Q30657670-6A57EDE1-7600-4652-9AE3-07A4CF7BB899Q30666491-64A72957-9251-4660-AA04-4372A091D74DQ30687080-39620ECF-A89E-4E4F-9019-8F61F659F178Q30746157-10C45DB0-85A0-47C4-BD81-7102540F4DE6Q30756414-432DC8E0-847D-4671-A678-63CE4D7B5E93Q30878103-30E0CE60-717F-4AD8-9574-6CB30EA7AF94Q30887191-376E2E42-2C10-4581-91F4-8641C17407DDQ30893529-58AD7F3F-CB74-4843-80B7-F15464749F37Q30930965-A7567876-4188-4E8F-BB5D-3072BBADFD65Q30967973-EF8F9E4F-CFFC-4450-A7DD-72963667FA38Q30971403-385CFD7C-D274-45ED-B511-0F12E8BA515CQ31063019-77A0F596-CF85-477D-AF78-CC5A4E4D49DDQ31080794-C3CE18A7-0FF2-42EA-9EDD-CEFDACDFC00BQ31145392-F357164F-DCFC-4D93-925E-6CA0F15552E0Q33440688-515A7E71-02EE-418A-B668-C49FD124668BQ33641176-C7940A23-5E6B-46DB-93DA-5AD2BDC33C0CQ33722365-508A4381-1CEA-44AF-B9EC-934A77CBB7FDQ33728978-43DAF2B3-8AA7-4CF7-8131-B21D29C79AD8Q33741405-EEFBD1DA-C36E-4C4F-8B97-28896D8ED798Q33758462-39939B37-0581-4E1B-AD81-DF727A16F490Q33782862-857BC4CF-3109-4C92-B37E-6FE349798741Q33788774-401B3207-97A4-4721-921E-1356A4CAE298Q33822830-DD54E784-DAA3-427B-9D2B-87EB1EBE7293Q33853042-D4063507-7C41-4CF1-BB7C-3F0D6D593FA8Q33891581-E19B6D5C-116D-410C-89D3-A171B2233986Q33981434-0326654E-6C9C-43A5-9F16-8D32792C4C2CQ33983021-DB678EA1-DBC5-4652-AB0A-54DCFFFF953C
P2860
Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database.
description
2010 nî lūn-bûn
@nan
2010 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
Automatic classification of pa ...... thods using the ADNI database.
@ast
Automatic classification of pa ...... thods using the ADNI database.
@en
Automatic classification of pa ...... thods using the ADNI database.
@nl
type
label
Automatic classification of pa ...... thods using the ADNI database.
@ast
Automatic classification of pa ...... thods using the ADNI database.
@en
Automatic classification of pa ...... thods using the ADNI database.
@nl
prefLabel
Automatic classification of pa ...... thods using the ADNI database.
@ast
Automatic classification of pa ...... thods using the ADNI database.
@en
Automatic classification of pa ...... thods using the ADNI database.
@nl
P2093
P921
P1433
P1476
Automatic classification of pa ...... thods using the ADNI database.
@en
P2093
Emilie Gerardin
Guillaume Auzias
Habib Benali
Jérôme Tessieras
Marie Chupin
Marie-Odile Habert
Olivier Colliot
Rémi Cuingnet
Stéphane Lehéricy
P304
P356
10.1016/J.NEUROIMAGE.2010.06.013
P407
P577
2010-06-11T00:00:00Z