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Items: 1 to 20 of 134

1.

Deep learning-based feature representation for AD/MCI classification.

Suk HI, Shen D.

Med Image Comput Comput Assist Interv. 2013;16(Pt 2):583-90.

2.

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis.

Suk HI, Lee SW, Shen D; Alzheimer’s Disease Neuroimaging Initiative.

Brain Struct Funct. 2015 Mar;220(2):841-59. doi: 10.1007/s00429-013-0687-3. Epub 2013 Dec 22.

3.

Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.

Suk HI, Lee SW, Shen D; Alzheimer's Disease Neuroimaging Initiative.

Neuroimage. 2014 Nov 1;101:569-82. doi: 10.1016/j.neuroimage.2014.06.077. Epub 2014 Jul 18.

4.

Multi-modality sparse representation-based classification for Alzheimer's disease and mild cognitive impairment.

Xu L, Wu X, Chen K, Yao L.

Comput Methods Programs Biomed. 2015 Nov;122(2):182-90. doi: 10.1016/j.cmpb.2015.08.004. Epub 2015 Aug 10.

PMID:
26298855
5.

Maximum-margin based representation learning from multiple atlases for Alzheimer's disease classification.

Min R, Cheng J, Price T, Wu G, Shen D.

Med Image Comput Comput Assist Interv. 2014;17(Pt 2):212-9.

6.

Inter-modality relationship constrained multi-task feature selection for AD/MCI classification.

Liu F, Wee CY, Chen H, Shen D.

Med Image Comput Comput Assist Interv. 2013;16(Pt 1):308-15.

7.

Multifold Bayesian kernelization in Alzheimer's diagnosis.

Liu S, Song Y, Cai W, Pujol S, Kikinis R, Wang X, Feng D.

Med Image Comput Comput Assist Interv. 2013;16(Pt 2):303-10.

8.

Clustering-induced multi-task learning for AD/MCI classification.

Suk HI, Shen D.

Med Image Comput Comput Assist Interv. 2014;17(Pt 3):393-400.

9.

Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification.

Liu F, Wee CY, Chen H, Shen D.

Neuroimage. 2014 Jan 1;84:466-75. doi: 10.1016/j.neuroimage.2013.09.015. Epub 2013 Sep 14.

10.

Manifold population modeling as a neuro-imaging biomarker: application to ADNI and ADNI-GO.

Guerrero R, Wolz R, Rao AW, Rueckert D; Alzheimer's Disease Neuroimaging Initiative (ADNI).

Neuroimage. 2014 Jul 1;94:275-86. doi: 10.1016/j.neuroimage.2014.03.036. Epub 2014 Mar 21.

PMID:
24657351
11.

Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer's disease.

Jie B, Zhang D, Cheng B, Shen D.

Med Image Comput Comput Assist Interv. 2013;16(Pt 1):275-83.

12.

Binary classification of ¹⁸F-flutemetamol PET using machine learning: comparison with visual reads and structural MRI.

Vandenberghe R, Nelissen N, Salmon E, Ivanoiu A, Hasselbalch S, Andersen A, Korner A, Minthon L, Brooks DJ, Van Laere K, Dupont P.

Neuroimage. 2013 Jan 1;64:517-25. doi: 10.1016/j.neuroimage.2012.09.015. Epub 2012 Sep 14.

PMID:
22982358
13.

Locally linear embedding (LLE) for MRI based Alzheimer's disease classification.

Liu X, Tosun D, Weiner MW, Schuff N; Alzheimer's Disease Neuroimaging Initiative.

Neuroimage. 2013 Dec;83:148-57. doi: 10.1016/j.neuroimage.2013.06.033. Epub 2013 Jun 21.

14.

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.

Dyrba M, Barkhof F, Fellgiebel A, Filippi M, Hausner L, Hauenstein K, Kirste T, Teipel SJ; EDSD study group.

J Neuroimaging. 2015 Sep-Oct;25(5):738-47. doi: 10.1111/jon.12214. Epub 2015 Jan 28.

PMID:
25644739
15.

Multimodal manifold-regularized transfer learning for MCI conversion prediction.

Cheng B, Liu M, Suk HI, Shen D, Zhang D; Alzheimer’s Disease Neuroimaging Initiative.

Brain Imaging Behav. 2015 Dec;9(4):913-26. doi: 10.1007/s11682-015-9356-x.

16.

Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease.

Zhang D, Shen D; Alzheimer's Disease Neuroimaging Initiative.

Neuroimage. 2012 Jan 16;59(2):895-907. doi: 10.1016/j.neuroimage.2011.09.069. Epub 2011 Oct 4. Erratum in: Neuroimage. 2012 Sep;62(3):2179.

17.

Multi-modality canonical feature selection for Alzheimer's disease diagnosis.

Zhu X, Suk HI, Shen D.

Med Image Comput Comput Assist Interv. 2014;17(Pt 2):162-9.

18.

A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease.

Tong T, Gao Q, Guerrero R, Ledig C, Chen L, Rueckert D, Initiative AD.

IEEE Trans Biomed Eng. 2017 Jan;64(1):155-165. doi: 10.1109/TBME.2016.2549363. Epub 2016 Apr 1.

PMID:
27046891
19.

Multiple instance learning for classification of dementia in brain MRI.

Tong T, Wolz R, Gao Q, Guerrero R, Hajnal JV, Rueckert D; Alzheimer’s Disease Neuroimaging Initiative.

Med Image Anal. 2014 Jul;18(5):808-18. doi: 10.1016/j.media.2014.04.006. Epub 2014 May 5.

PMID:
24858570
20.

A Robust Deep Model for Improved Classification of AD/MCI Patients.

Li F, Tran L, Thung KH, Ji S, Shen D, Li J.

IEEE J Biomed Health Inform. 2015 Sep;19(5):1610-6. doi: 10.1109/JBHI.2015.2429556. Epub 2015 May 4.

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