20.9.2 Brain, Cortex, Alzheimer's Disease, Dementia

Freeborough, P.A., Fox, N.C.,
MR Image Texture Analysis Applied to the Diagnosis and Tracking of Alzheimers-Disease,
MedImg(17), No. 3, June 1998, pp. 475-479.
IEEE Top Reference. 9809
BibRef

Freeborough, P.A.[Peter A.],
A Comparison of Fractal Texture Descriptors,
BMVC97(xx-yy).
HTML Version. 0209
BibRef

Bhattacharya, M., Majumder, D.D.,
Registration of CT and MR images of Alzheimer's patient: A Shape Theoretic Approach,
PRL(21), No. 6-7, June 2000, pp. 531-548. 0006
BibRef

Studholme, C., Cardenas, V., Song, E., Ezekiel, F., Maudsley, A.A., Weiner, M.,
Accurate Template-Based Correction of Brain MRI Intensity Distortion With Application to Dementia and Aging,
MedImg(23), No. 1, January 2004, pp. 99-110.
IEEE Abstract. 0403
BibRef

Wang, L.[Lei], Beg, F.[Faisal], Ratnanather, J.T.[J. Tilak], Ceritoglu, C.[Can], Younes, L.[Laurent], Morris, J.C.[John C.], Csernansky, J.G.[John G.], Miller, M.I.[Michael I.],
Large Deformation Diffeomorphism and Momentum Based Hippocampal Shape Discrimination in Dementia of the Alzheimer type,
MedImg(26), No. 4, April 2007, pp. 462-470.
IEEE DOI 0704

See also Diffeomorphic Active Contours. BibRef

Tward, D.[Daniel], Miller, M., Trouvé, A.[Alain], Younes, L.[Laurent],
Parametric Surface Diffeomorphometry for Low Dimensional Embeddings of Dense Segmentations and Imagery,
PAMI(39), No. 6, June 2017, pp. 1195-1208.
IEEE DOI 1705
Biological system modeling, Diseases, Hippocampus, Image segmentation, Magnetic resonance imaging, Measurement, Shape, Computational anatomy, diffeomorphometry, medical imaging, neuroimaging, shape, analysis BibRef

Tward, D.[Daniel], Jovicich, J.[Jorge], Soricelli, A.[Andrea], Frisoni, G.[Giovanni], Trouvé, A.[Alain], Younes, L.[Laurent], Miller, M.[Michael],
Improved Reproducibility of Neuroanatomical Definitions through Diffeomorphometry and Complexity Reduction,
MLMI14(223-230).
Springer DOI 1410
BibRef

Tu, Z.W.[Zhuo-Wen], Zheng, S.F.[Song-Feng], Yuille, A.L.[Alan L.], Reiss, A.L.[Allan L.], Dutton, R.A.[Rebecca A.], Lee, A.D.[Agatha D.], Galaburda, A.M.[Albert M.], Dinov, I.D.[Ivo D.], Thompson, P.M.[Paul M.], Toga, A.W.[Arthur W.],
Automated Extraction of the Cortical Sulci Based on a Supervised Learning Approach,
MedImg(26), No. 4, April 2007, pp. 541-552.
IEEE DOI 0704
BibRef

Shi, Y.G., Tu, Z., Reiss, A.L., Dutton, R.A., Lee, A.D., Galaburda, A.M., Dinov, I.D., Thompson, P.M., Toga, A.W.,
Joint Sulcal Detection on Cortical Surfaces With Graphical Models and Boosted Priors,
MedImg(28), No. 3, March 2009, pp. 361-373.
IEEE DOI 0903
BibRef

Liu, X.Y.[Xin-Yang], Liu, X.W.[Xiu-Wen], Shi, Y.G.[Yong-Gang], Thompson, P.M.[Paul M.], Mio, W.[Washington],
A Model of Volumetric Shape for the Analysis of Longitudinal Alzheimer's Disease Data,
ECCV10(III: 594-606).
Springer DOI 1009
BibRef

Alvarez Illan, I., Gorriz, J.M., Ramirez, J., Salas-Gonzalez, D., Lopez, M., Segovia, F., Padilla, P., Puntonet, C.G.,
Projecting independent components of SPECT images for computer aided diagnosis of Alzheimer's disease,
PRL(31), No. 11, 1 August 2010, pp. 1342-1347.
Elsevier DOI 1008
Alzheimer's disease; Independent Component Analysis; Computer aided diagnosis; Support vector machine; Supervised learning BibRef

Padilla, P., Lopez, M., Gorriz, J.M., Ramirez, J., Salas-Gonzalez, D., Alvarez, I.,
NMF-SVM Based CAD Tool Applied to Functional Brain Images for the Diagnosis of Alzheimer's Disease,
MedImg(31), No. 2, February 2012, pp. 207-216.
IEEE DOI 1202
BibRef

Salas-Gonzalez, D., Gorriz, J.M., Ramirez, J., Alvarez, I., Lopez, M., Segovia, F., Gomez-Rio, M.,
Skewness as feature for the diagnosis of Alzheimer's disease using SPECT images,
ICIP09(837-840).
IEEE DOI 0911
BibRef

Ye, J.P.[Jie-Ping], Wu, T.[Teresa], Li, J.[Jing], Chen, K.W.[Ke-Wei],
Machine Learning Approaches for the Neuroimaging Study of Alzheimer's Disease,
Computer(44), No. 4, April 2011, pp. 99-101.
IEEE DOI 1104
BibRef

Filipovych, R.[Roman], Wang, Y.[Ying], Davatzikos, C.[Christos],
Pattern analysis in neuroimaging: Beyond two-class categorization,
IJIST(21), No. 2, June 2011, pp. 173-178.
DOI Link 1101
clustering; MRI; aging; MCI; Alzheimer's disease BibRef

Pachauri, D., Hinrichs, C., Chung, M.K., Johnson, S.C., Singh, V.,
Topology-Based Kernels With Application to Inference Problems in Alzheimer's Disease,
MedImg(30), No. 10, October 2011, pp. 1760-1770.
IEEE DOI 1110
BibRef

Chaves, R., Ramírez, J., Górriz, J.M., Illán, I.A., Initiative, T.A.D.N.[The Alzheimers Disease Neuroimaging],
Functional brain image classification using association rules defined over discriminant regions,
PRL(33), No. 12, 1 September 2012, pp. 1666-1672.
Elsevier DOI 1208
Functional brain imaging; Alzheimer's Disease; Fisher Discriminant Ratio; Association rules BibRef

Mesrob, L., Magnin, B., Colliot, O., Sarazin, M., Hahn-Barma, V., Dubois, B., Gallinari, P., Lehericy, S., Kinkingnehun, S., Benali, H.,
Identification of atrophy patterns in Alzheimer's disease based on SVM feature selection and anatomical parcellation,
BMVA(2009), No. 7, 2009, pp. 1-9.
PDF File. 1209
BibRef

Janousova, E.[Eva], Vounou, M.[Maria], Wolz, R.[Robin], Gray, K.R.[Katherine R.], Rueckert, D.[Daniel], Montana, G.[Giovanni],
Biomarker discovery for sparse classification of brain images in Alzheimer's disease,
BMVA(2012), No. 2, 2012, pp. 1-11.
PDF File. 1209
BibRef

Cuingnet, R.[Rémi], Glaunès, J.A.[Joan Alexis], Chupin, M.[Marie], Benali, H.[Habib], Colliot, O.[Olivier],
Spatial and Anatomical Regularization of SVM: A General Framework for Neuroimaging Data,
PAMI(35), No. 3, March 2013, pp. 682-696.
IEEE DOI 1303
BibRef
Earlier:
Anatomical Regularization on Statistical Manifolds for the Classification of Patients with Alzheimer's Disease,
MLMI11(201-208).
Springer DOI 1109
BibRef

Zhao, M.B.[Ming-Bo], Chan, R.H.M., Tang, P.[Peng], Chow, T.W.S., Wong, S.W.H.,
Trace Ratio Linear Discriminant Analysis for Medical Diagnosis: A Case Study of Dementia,
SPLetters(20), No. 5, May 2013, pp. 431-434.
IEEE DOI 1304
BibRef

Zhao, M.B.[Ming-Bo], Chan, R.H.M., Chow, T.W.S., Tang, P.,
Compact Graph based Semi-Supervised Learning for Medical Diagnosis in Alzheimer's Disease,
SPLetters(21), No. 10, October 2014, pp. 1192-1196.
IEEE DOI 1407
Classification algorithms BibRef

Huang, S.[Shuai], Li, J.[Jing], Ye, J.P.[Jie-Ping], Fleisher, A.[Adam], Chen, K.W.[Ke-Wei], Wu, T.[Teresa], Reiman, E.[Eric],
A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data,
PAMI(35), No. 6, June 2013, pp. 1328-1342.
IEEE DOI 1305
the Alzheimer's Disease Neuroimaging Initiative. Apply to brain connectivity modeling. BibRef

Komlagan, M.[Mawulawoé], Ta, V.T.[Vinh-Thong], Pan, X.Y.[Xing-Yu], Domenger, J.P.[Jean-Philippe], Coupé, D.L.C.P.[D. Louis Collins Pierrick],
Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer's Disease,
MLMI14(141-148).
Springer DOI 1410
the Alzheimer's Disease Neuroimaging Initiative BibRef

Kodewitz, A.[Andreas], Lelandais, S.[Sylvie], Montagne, C.[Christophe], Vigneron, V.[Vincent],
Alzheimer's disease early detection from sparse data using brain importance maps,
ELCVIA(12), No. 1, 2013, pp. xx-yy.
DOI Link 1307
BibRef

Xie, J.[Jing], Fletcher, E.[Evan], Singh, B.[Baljeet], Carmichael, O.[Owen],
Robust measurement of individual localized changes to the aging hippocampus,
CVIU(117), No. 9, 2013, pp. 1128-1137.
Elsevier DOI 1307
Hippocampal shape change BibRef

Morabito, F.C.[Francesco Carlo],
The compressibility of an electroencephalography signal may indicate Alzheimer's disease,
SPIE(Newsroom), June 3, 2013
DOI Link 1307
By analyzing the content of electrical activity at the surface of the brain, researchers can distinguish between patients who are healthy and those with different types of cognitive impairment. BibRef

Ortiz, A.[Andrés], Górriz, J.M.[Juan M.], Ramírez, J.[Javier], Martínez-Murcia, F.J.,
LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease,
PRL(34), No. 14, 2013, pp. 1725-1733.
Elsevier DOI 1308
Alzheimer's disease BibRef

Zeng, W.[Wei], Shi, R.[Rui], Wang, Y.L.[Ya-Lin], Yau, S.T.[Shing-Tung], Gu, X.F.[Xian-Feng],
Teichmüller Shape Descriptor and Its Application to Alzheimer's Disease Study,
IJCV(105), No. 2, November 2013, pp. 155-170.
Springer DOI 1309
BibRef

Dai, D.[Dai], He, H.G.[Hui-Guang], Vogelstein, J.T.[Joshua T.], Hou, Z.G.[Zeng-Guang],
Accurate prediction of AD patients using cortical thickness networks,
MVA(24), No. 7, October 2013, pp. 1445-1457.
Springer DOI 1309
BibRef
Earlier:
Network-Based Classification Using Cortical Thickness of AD Patients,
MLMI11(193-200).
Springer DOI 1109
Alzheimers BibRef

Osadebey, M.[Michael], Bouguila, N.[Nizar], Arnold, D.[Douglas], And: Initiative, T.A.D.N.[The Alzheimers Disease Neuroimaging],
The clique potential of Markov random field in a random experiment for estimation of noise levels in 2D brain MRI,
IJIST(23), No. 4, 2013, pp. 304-313.
DOI Link 1312
magnetic resonance imaging BibRef

Osadebey, M.[Michael], Bouguila, N.[Nizar], Arnold, D.[Douglas], And: Initiative, T.A.D.N.[The Alzheimers Disease Neuroimaging],
Four-neighborhood clique kernel: A general framework for Bayesian and variational techniques of noise reduction in magnetic resonance images of the brain,
IJIST(24), No. 3, 2014, pp. 224-238.
DOI Link 1408
magnetic resonance imaging BibRef

Zhao, X.J.[Xiao-Jie], Wen, X.T.[Xiao-Tong], Shen, J.H.[Jia-Hui], Hong, H.[Hao], Yao, L.[Li],
An improved fast marching method and its application in Alzheimer's disease,
IJIST(23), No. 4, 2013, pp. 346-352.
DOI Link 1312
fast marching method BibRef

Rueda, A., Gonzalez, F.A., Romero, E.,
Extracting Salient Brain Patterns for Imaging-Based Classification of Neurodegenerative Diseases,
MedImg(33), No. 6, June 2014, pp. 1262-1274.
IEEE DOI 1407
Brain modeling BibRef

Wan, J., Zhang, Z., Rao, B.D., Fang, S., Yan, J., Saykin, A.J., Shen, L.,
Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning,
MedImg(33), No. 7, July 2014, pp. 1475-1487.
IEEE DOI 1407
Alzheimer's disease BibRef

Yang, W.[Wenji], Huang, W.[Wei], Chen, S.X.[Shan-Xue],
Partial Volume Correction on ASL-MRI and Its Application on Alzheimer's Disease Diagnosis,
IEICE(E97-D), No. 11, November 2014, pp. 2912-2918.
WWW Link. 1412
BibRef

Poynton, C.B., Jenkinson, M., Adalsteinsson, E., Sullivan, E.V., Pfefferbaum, A., Wells, W.,
Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation With Brain Iron in Normal Aging,
MedImg(34), No. 1, January 2015, pp. 339-353.
IEEE DOI 1502
Fourier analysis BibRef

Liu, X.W.[Xin-Wang], Zhou, L.P.[Lu-Ping], Wang, L.[Lei], Zhang, J.[Jian], Yin, J.P.[Jian-Ping], Shen, D.G.[Ding-Gang],
An efficient radius-incorporated MKL algorithm for Alzheimer's disease prediction,
PR(48), No. 7, 2015, pp. 2141-2150.
Elsevier DOI 1504
Multiple kernel learning BibRef

Aggarwal, N.[Namita], Rana, B.[Bharti], Agrawal, R.K.,
3d discrete wavelet transform for computer aided diagnosis of Alzheimer's disease using t1-weighted brain MRI,
IJIST(25), No. 2, 2015, pp. 179-190.
DOI Link 1506
BibRef
Earlier:
Computer Aided Diagnosis of Alzheimer's Disease from MRI Brain Images,
ICIAR12(II: 259-267).
Springer DOI 1206
Alzheimer's disease BibRef

Li, Y., Pan, J., Long, J., Yu, T., Wang, F., Yu, Z., Wu, W.,
Multimodal BCIs: Target Detection, Multidimensional Control, and Awareness Evaluation in Patients With Disorder of Consciousness,
PIEEE(104), No. 2, February 2016, pp. 332-352.
IEEE DOI 1601
Biomedical signal processing BibRef

Warren, D.J., Kellis, S., Nieveen, J.G., Wendelken, S.M., Dantas, H., Davis, T.S., Hutchinson, D.T., Normann, R.A., Clark, G.A., Mathews, V.J.,
Recording and Decoding for Neural Prostheses,
PIEEE(104), No. 2, February 2016, pp. 374-391.
IEEE DOI 1601
Biomedical signal processing BibRef

Seo, D.H.[Do-Hyung], Ho, J.[Jeffrey], Vemuri, B.C.[Baba C.],
Covariant Image Representation with Applications to Classification Problems in Medical Imaging,
IJCV(116), No. 2, January 2016, pp. 190-209.
Springer DOI 1602
Apply to MR for Alzheimers and MR detection of seizures. BibRef

Liu, M.H.[Man-Hua], Zhang, D.Q.[Dao-Qiang], Shen, D.G.[Ding-Gang],
Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment,
MedImg(35), No. 6, June 2016, pp. 1463-1474.
IEEE DOI 1606
BibRef
Earlier:
Inherent Structure-Guided Multi-view Learning for Alzheimer's Disease and Mild Cognitive Impairment Classification,
MLMI15(296-303).
Springer DOI 1511
Alzheimer's disease BibRef

Jie, B.[Biao], Liu, M.X.[Ming-Xia], Zhang, D.Q.[Dao-Qiang], Shen, D.G.[Ding-Gang],
Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis,
IP(27), No. 5, May 2018, pp. 2340-2353.
IEEE DOI 1804
biomedical MRI, brain, cognition, diseases, graph theory, image classification, medical image processing, neurophysiology, mild cognitive impairment (MCI) BibRef

Cheng, B.[Bo], Liu, M.X.[Ming-Xia], Zhang, D.Q.[Dao-Qiang],
Multimodal Multi-label Transfer Learning for Early Diagnosis of Alzheimer's Disease,
MLMI15(238-245).
Springer DOI 1511
BibRef

Liu, M.X.[Ming-Xia], Zhang, D.Q.[Dao-Qiang], Adeli-Mosabbeb, E.[Ehsan], Shen, D.G.[Ding-Gang],
Relationship Induced Multi-atlas Learning for Alzheimer's Disease Diagnosis,
MCV15(24-33).
Springer DOI 1608
BibRef

Liu, M.H.[Man-Hua], Zhang, D.Q.[Dao-Qiang], Yap, P.T.[Pew-Thian], Shen, D.G.[Ding-Gang],
Hierarchical Ensemble of Multi-level Classifiers for Diagnosis of Alzheimer's Disease,
MLMI12(27-35).
Springer DOI 1211
BibRef

Suk, H.I.[Heung-Il], Shen, D.G.[Ding-Gang],
Deep Ensemble Sparse Regression Network for Alzheimer's Disease Diagnosis,
MLMI16(113-121).
Springer DOI 1611
BibRef

Zhu, X.F.[Xiao-Feng], Suk, H.I.[Heung-Il], Zhu, Y.H.[Yong-Hua], Thung, K.H.[Kim-Han], Wu, G.R.[Guo-Rong], Shen, D.G.[Ding-Gang],
Multi-view Classification for Identification of Alzheimer's Disease,
MLMI15(255-262).
Springer DOI 1511
BibRef
Earlier: A1, A2, A6, Only:
Sparse Discriminative Feature Selection for Multi-class Alzheimer's Disease Classification,
MLMI14(157-164).
Springer DOI 1410
BibRef

Zhu, X.F.[Xiao-Feng], Suk, H.I.[Heung-Il], Thung, K.H.[Kim-Han], Zhu, Y.Y.[Ying-Ying], Wu, G.R.[Guo-Rong], Shen, D.G.[Ding-Gang],
Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis,
MLMI16(77-85).
Springer DOI 1611
BibRef

Zhu, X.F.[Xiao-Feng], Thung, K.H.[Kim-Han], Zhang, J.[Jun], Shen, D.G.[Ding-Gang],
Fast Neuroimaging-Based Retrieval for Alzheimer's Disease Analysis,
MLMI16(313-321).
Springer DOI 1611
BibRef

Zhang, D.Q.[Dao-Qiang], Shen, D.G.[Ding-Gang],
MultiCost: Multi-stage Cost-sensitive Classification of Alzheimer's Disease,
MLMI11(344-351).
Springer DOI 1109
BibRef

Jie, B.[Biao], Zhang, D.Q.[Dao-Qiang], Wee, C.Y.[Chong-Yaw], Shen, D.G.[Ding-Gang],
Structural Feature Selection for Connectivity Network-Based MCI Diagnosis,
MBIA12(175-184).
Springer DOI 1210
BibRef

Zhou, L.P.[Lu-Ping], Wang, Y.P.[Ya-Ping], Li, Y.[Yang], Yap, P.T.[Pew-Thian], Shen, D.G.[Ding-Gang], Adni,
Hierarchical anatomical brain networks for MCI prediction by partial least square analysis,
CVPR11(1073-1080).
IEEE DOI 1106
T1-weighted MRI for mild cognitive impairment. BibRef

Li, Z., Suk, H.I., Shen, D., Li, L.,
Sparse Multi-Response Tensor Regression for Alzheimer's Disease Study With Multivariate Clinical Assessments,
MedImg(35), No. 8, August 2016, pp. 1927-1936.
IEEE DOI 1608
Alzheimer's disease BibRef

Zhou, L.P.[Lu-Ping], Wang, L.[Lei], Liu, L.Q.[Ling-Qiao], Ogunbona, P.O.[Philip O.], Shen, D.G.[Ding-Gang],
Learning Discriminative Bayesian Networks from High-Dimensional Continuous Neuroimaging Data,
PAMI(38), No. 11, November 2016, pp. 2269-2283.
IEEE DOI 1610
BibRef
Earlier:
Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network,
CVPR13(2243-2250)
IEEE DOI 1309
Bayes methods. Alzheimer's Disease BibRef

Zhou, L.P.[Lu-Ping], Wang, L.[Lei], Ogunbona, P.O.[Philip O.],
Discriminative Sparse Inverse Covariance Matrix: Application in Brain Functional Network Classification,
CVPR14(3097-3104)
IEEE DOI 1409
Graphical LASSO BibRef

Tong, T.[Tong], Gray, K.[Katherine], Gao, Q.[Qinquan], Chen, L.[Liang], Rueckert, D.[Daniel],
Multi-modal classification of Alzheimer's disease using nonlinear graph fusion,
PR(63), No. 1, 2017, pp. 171-181.
Elsevier DOI 1612
BibRef
Earlier:
Nonlinear Graph Fusion for Multi-modal Classification of Alzheimer's Disease,
MLMI15(77-84).
Springer DOI 1511
Multiple modalities BibRef

Shi, B.[Bibo], Chen, Y.[Yani], Zhang, P.[Pin], Smith, C.D.[Charles D.], Liu, J.D.[Jun-Dong],
Nonlinear Feature Transformation and Deep Fusion for Alzheimer's Disease Staging Analysis,
PR(63), No. 1, 2017, pp. 487-498.
Elsevier DOI 1612
BibRef
And: Erratum: PR(66), No. 1, 2017, pp. 447-.
Elsevier DOI 1704
BibRef
Earlier: A2, A1, A4, A5, Only: MLMI15(304-312).
Springer DOI 1511
Metric learning BibRef

Shi, B.[Bibo], Chen, Y.[Yani], Hobbs, K.[Kevin], Smith, C.D.[Charles D.], Liu, J.D.[Jun-Dong],
Nonlinear Metric Learning for Alzheimer’s Disease Diagnosis with Integration of Longitudinal Neuroimaging Features,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Guerrero, R., Ledig, C., Schmidt-Richberg, A., Rueckert, D.,
Group-constrained manifold learning: Application to AD risk assessment,
PR(63), No. 1, 2017, pp. 570-582.
Elsevier DOI 1612
Alzheimer's disease BibRef

Zhang, J., Gao, Y., Gao, Y., Munsell, B.C., Shen, D.,
Detecting Anatomical Landmarks for Fast Alzheimer's Disease Diagnosis,
MedImg(35), No. 12, December 2016, pp. 2524-2533.
IEEE DOI 1612
Feature extraction BibRef

Nanni, L.[Loris], Salvatore, C.[Christian], Cerasa, A.[Antonio], Castiglioni, I.[Isabella],
Combining multiple approaches for the early diagnosis of Alzheimer's Disease,
PRL(84), No. 1, 2016, pp. 259-266.
Elsevier DOI 1612
Alzheimer's Disease BibRef

Lei, B., Yang, P., Wang, T., Chen, S., Ni, D.,
Relational-Regularized Discriminative Sparse Learning for Alzheimer's Disease Diagnosis,
Cyber(47), No. 4, April 2017, pp. 1102-1113.
IEEE DOI 1704
Cybernetics BibRef

Alam, S.[Saruar], Kwon, G.R.[Goo-Rak], Initiative, T.A.D.N.[The Alzheimer's Disease Neuroimaging],
Alzheimer disease classification using KPCA, LDA, and multi-kernel learning SVM,
IJIST(27), No. 2, 2017, pp. 133-143.
DOI Link 1706
FreeSurfer, CIVET, KPCA, PCA, LDA, MK-SVM BibRef

Dadar, M., Pascoal, T.A., Manitsirikul, S., Misquitta, K., Fonov, V.S., Tartaglia, M.C., Breitner, J., Rosa-Neto, P., Carmichael, O.T., Decarli, C., Collins, D.L.,
Validation of a Regression Technique for Segmentation of White Matter Hyperintensities in Alzheimer's Disease,
MedImg(36), No. 8, August 2017, pp. 1758-1768.
IEEE DOI 1708
Alzheimer's disease, Image segmentation, Lesions, Magnetic resonance imaging, Robustness, Alzheimer's disease, White matter hyperintensities, aging, segmentation BibRef

Cao, P.[Peng], Shan, X.F.[Xuan-Feng], Zhao, D.[Dazhe], Huang, M.[Min], Zaiane, O.[Osmar],
Sparse shared structure based multi-task learning for MRI based cognitive performance prediction of Alzheimer's disease,
PR(72), No. 1, 2017, pp. 219-235.
Elsevier DOI 1708
Alzheimer's, disease BibRef

Cao, P.[Peng], Liu, X.L.[Xiao-Li], Yang, J.Z.[Jin-Zhu], Zhao, D.Z.[Da-Zhe], Huang, M.[Min], Zaiane, O.[Osmar],
L2,1-l1 regularized nonlinear multi-task representation learning based cognitive performance prediction of Alzheimer's disease,
PR(79), 2018, pp. 195-215.
Elsevier DOI 1804
Alzheimer's disease, Regression, Sparse learning, Multi-task learning, Kernel method BibRef

Abdullah, S., Choudhury, T.,
Sensing Technologies for Monitoring Serious Mental Illnesses,
MultMedMag(25), No. 1, January 2018, pp. 61-75.
IEEE DOI 1804
Biomedical monitoring, Biosensors, Global Positioning System, Mental disorders, Multimedia communication, Sensors, sensing BibRef

Alvarez, F., Popa, M., Solachidis, V., Hernández-Peñaloza, G., Belmonte-Hernández, A., Asteriadis, S., Vretos, N., Quintana, M., Theodoridis, T., Dotti, D., Daras, P.,
Behavior Analysis through Multimodal Sensing for Care of Parkinson's and Alzheimer's Patients,
MultMedMag(25), No. 1, January 2018, pp. 14-25.
IEEE DOI 1804
Alzheimer's disease, Behavioral sciences, Biomedical imaging, Calibration, Feature extraction, Patient monitoring, Sensors, wireless sensor networks BibRef

Alvarez, F., Popa, M., Vretos, N., Belmonte-Hernández, A., Asteriadis, S., Solachidis, V., Mariscal, T., Dotti, D., Daras, P.,
Multimodal monitoring of Parkinson's and Alzheimer's patients using the ICT4LIFE platform,
AVSS17(1-6)
IEEE DOI 1806
Internet of Things, diseases, feature extraction, medical computing, patient monitoring, sensor fusion, Wireless sensor networks BibRef

Strickland, E.,
The digital fingerprints of brain disorders,
Spectrum(55), No. 5, May 2018, pp. 12-13.
IEEE DOI 1805
[News] BibRef

Asim, Y.[Yousra], Raza, B.[Basit], Malik, A.K.[Ahmad Kamran], Rathore, S.[Saima], Hussain, L.[Lal], Iftikhar, M.A.[Mohammad Aksam],
A multi-modal, multi-atlas-based approach for Alzheimer detection via machine learning,
IJIST(28), No. 2, 2018, pp. 113-123.
WWW Link. 1806
BibRef

Kahindo, C., El-Yacoubi, M.A., Garcia-Salicetti, S., Rigaud, A., Cristancho-Lacroix, V.,
Characterizing Early-Stage Alzheimer Through Spatiotemporal Dynamics of Handwriting,
SPLetters(25), No. 8, August 2018, pp. 1136-1140.
IEEE DOI 1808
Bayes methods, diseases, feature extraction, handwritten character recognition, neurophysiology, probabilistic modeling BibRef

El-Yacoubi, M.A.[Mounîm A.], Garcia-Salicetti, S.[Sonia], Kahindo, C.[Christian], Rigaud, A.S.[Anne-Sophie], Cristancho-Lacroix, V.[Victoria],
From aging to early-stage Alzheimer's: Uncovering handwriting multimodal behaviors by semi-supervised learning and sequential representation learning,
PR(86), 2019, pp. 112-133.
Elsevier DOI 1811
Online handwriting, Mild Cognitive Impairment, Aging, Unsupervised & semi-supervised learning, Temporal representation learning BibRef

Mishra, S.[Shiwangi], Beheshti, I.[Iman], Khanna, P.[Pritee],
A statistical region selection and randomized volumetric features selection framework for early detection of Alzheimer's disease,
IJIST(28), No. 4, December 2018, pp. 302-314.
WWW Link. 1811
Alzheimer's Disease Neuroimaging Initiative BibRef

Baumgartner, C.F., Koch, L.M., Tezcan, K.C., Ang, J.X.,
Visual Feature Attribution Using Wasserstein GANs,
CVPR18(8309-8319)
IEEE DOI 1812
Visualization, Biomedical imaging, Alzheimer's disease, Neural networks, Neuroimaging BibRef

Islam, J., Zhang, Y.,
Early Diagnosis of Alzheimer's Disease: A Neuroimaging Study with Deep Learning Architectures,
WiCV18(1962-19622)
IEEE DOI 1812
Alzheimer's disease, Magnetic resonance imaging, Brain modeling, Training, Medical diagnosis BibRef

Zhang, Y.[Yu], Zhang, H.[Han], Chen, X.B.[Xiao-Bo], Liu, M.X.[Ming-Xia], Zhu, X.F.[Xiao-Feng], Lee, S.W.[Seong-Whan], Shen, D.G.[Ding-Gang],
Strength and similarity guided group-level brain functional network construction for MCI diagnosis,
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Alzheimers disease, Mild cognitive impairment, Resting-state functional magnetic resonance imaging (rs-fMRI), Diagnosis BibRef

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IEEE DOI 1902
Imaging, Training, Testing, Diseases, Data models, Training data, Neuroimaging, Hyper-graph learning, computer assisted diagnosis, neurodegenerative disease BibRef

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IEEE DOI 1903
Dementia, Image reconstruction, Interpolation, Training, Magnetic resonance imaging, Tau, PET Imaging, SUVR, scan-time error, Alzheimer's disease BibRef

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IEEE DOI 1905
Feature extraction, Time series analysis, Functional magnetic resonance imaging, Diseases, machine learning BibRef

Neffati, S.[Syrine], Ben Abdellafou, K.[Khaoula], Jaffel, I.[Ines], Taouali, O.[Okba], Bouzrara, K.[Kais],
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IEEE DOI 1906
Genomics, Bioinformatics, Biomedical imaging, Diseases, Neuroimaging, Mental disorders, Precision engineering, Predictive models, transdiagnostic BibRef

Wang, P., Liu, Y., Shen, D.,
Flexible Locally Weighted Penalized Regression With Applications on Prediction of Alzheimer's Disease Neuroimaging Initiative's Clinical Scores,
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IEEE DOI 1906
Kernel, Diseases, Sociology, Statistics, Brain modeling, Forestry, Training, Heterogeneity, local models, ordinal classification, random forests BibRef

Khagi, B.[Bijen], Kwon, G.R.[Goo-Rak], Lama, R.[Ramesh],
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Alzheimer's disease, Maxwell-Boltzmann statistics, Network edge entropy BibRef

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Latent Representation Learning for Alzheimer's Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data,
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IEEE DOI 1910
Magnetic resonance imaging, Feature extraction, Genetics, Neuroimaging, Alzheimer's disease, Positron emission tomography, latent representation space BibRef

Huang, W., Luo, M., Liu, X., Zhang, P., Ding, H., Xue, W., Ni, D.,
Arterial Spin Labeling Images Synthesis From sMRI Using Unbalanced Deep Discriminant Learning,
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IEEE DOI 1910
Deep learning, Dementia, Biomedical imaging, Medical diagnosis, Magnetic resonance imaging, Magnetic resonance imaging (MRI), machine learning BibRef

Li, J.[Jiaye], Wu, L.[Lin], Wen, G.Q.[Guo-Qiu], Li, Z.[Zhi],
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Alzheimer's Disease, Multi-view, Exclusive lasso learning, Feature selection, Sparse learning BibRef

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Choi, J.Y., Lee, B.,
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Alzheimer's disease classification, ensemble deep learning, generalization loss BibRef

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Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection,
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Ions, Noise measurement, Manganese, Diagnosis, convolutional neural networks, brain network, functional MRI BibRef

Lei, B.Y.[Bai-Ying], Yang, M.Y.[Meng-Ya], Yang, P.[Peng], Zhou, F.[Feng], Hou, W.[Wen], Zou, W.B.[Wen-Bin], Li, X.[Xia], Wang, T.F.[Tian-Fu], Xiao, X.H.[Xiao-Hua], Wang, S.Q.[Shu-Qiang],
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Alzheimer's disease, Longitudinal scores prediction, Joint learning, Correntropy, Deep polynomial network BibRef

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Feature extraction, Solid modeling, Atrophy, Brain modeling, Alzheimer's disease, Medical diagnosis, Support vector machines, structural MRI BibRef

Zhang, J.[Jun], Liu, M.X.[Ming-Xia], An, L.[Le], Gao, Y.Z.[Yao-Zong], Shen, D.G.[Ding-Gang],
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Alzheimer's disease, dementia, visualisation, system identification, machine learning BibRef

Zhang, Y.[Yingteng], Liu, S.[Shenquan], Yu, X.L.[Xiao-Li],
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Alzheimer's disease, gray matter volume, longitudinal analysis, longitudinal classification, mild cognitive impairment BibRef

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Alzheimer's disease, biomarker identification, joint regression-classification, longitudinal, multi-modal, multi-task BibRef

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Magnetic resonance imaging, Feature extraction, Diseases, Positron emission tomography, Medical diagnosis, Brain modeling, PET BibRef

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Springer DOI 2010
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CVPR18(9271-9280)
IEEE DOI 1812
Shape, Trajectory, Computational modeling, Manifolds, Spatiotemporal phenomena, Data models, Numerical models BibRef

Debavelaere, V.[Vianney], Durrleman, S.[Stanley], Allassonnière, S.[Stéphanie], Initiative, T.A.D.N.[The Alzheimer's Disease Neuroimaging],
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Imaging, Genetics, Task analysis, Brain, Neuroimaging, Dementia, Brain imaging genetics, sparse canonical correlation analysis, the dirty multi-task SCCA BibRef

Saied, I., Arslan, T., Chandran, S., Smith, C., Spires-Jones, T., Pal, S.,
Non-Invasive RF Technique for Detecting Different Stages of Alzheimer's Disease and Imaging Beta-Amyloid Plaques and Tau Tangles in the Brain,
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Dielectric measurement, Dielectrics, Brain modeling, Radio frequency, Sensors, Computational modeling, Permittivity, radio frequency BibRef

Li, Y., Liu, J., Tang, Z., Lei, B.,
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Time series analysis, Feature extraction, Diffusion tensor imaging, Adaptive systems, Topology, Diseases, spatial-temporal feature BibRef

Yang, P., Zhou, F., Ni, D., Xu, Y., Chen, S., Wang, T., Lei, B.,
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IEEE DOI 2012
Manifolds, Harmonic analysis, Diseases, Laplace equations, Optimization, Neuroimaging, Algebra, Brain network, computer-assisted diagnosis BibRef

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Matrix Classifier On Dynamic Functional Connectivity For Mci Identification,
ICIP20(325-329)
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Feature extraction, Correlation, Dementia, Spatiotemporal phenomena, Training, Alzheimer's disease (AD), support matrix machines (SMM) BibRef

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Coarse-to-Fine Hamiltonian Dynamics of Hierarchical Flows in Computational Anatomy,
Diff-CVML20(3760-3765)
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Lu, L., Wang, H., Elbeleidy, S., Nie, F.,
Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers,
CVPR20(4826-4835)
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Neuroimaging, Biomarkers, Dementia, Brain modeling, Biomedical imaging BibRef

Slapnicar, G., Dovgan, E., Cuk, P., Lustrek, M.,
Contact-Free Monitoring of Physiological Parameters in People With Profound Intellectual and Multiple Disabilities,
CVPM19(1664-1672)
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Videos, Skin, Biomedical monitoring, Heart rate, Databases, Blood, Physiology, physiological signals, PIMD, deep learning, LSTM, rPPG, video cameras BibRef

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Generative Aging of Brain MR-Images and Prediction of Alzheimer Progression,
GCPR19(247-260).
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Ge, C., Qu, Q., Gu, I.Y., Store Jakola, A.,
Multiscale Deep Convolutional Networks for Characterization and Detection of Alzheimer's Disease Using MR images,
ICIP19(789-793)
IEEE DOI 1910
Alzheimer's disease detection, MR images, multiscale features, multiscale CNN, feature fusion and enhancement BibRef

Dentamaro, V.[Vincenzo], Impedovo, D.[Donato], Pirlo, G.[Giuseppe],
Real-Time Neurodegenerative Disease Video Classification with Severity Prediction,
CIAP19(II:618-628).
Springer DOI 1909
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Cilia, N.D.[Nicole Dalia], de Stefano, C.[Claudio], Fontanella, F.[Francesco], Molinara, M.[Mario], di Freca, A.S.[Alessandra Scotto],
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CIAP19(II:683-693).
Springer DOI 1909
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Plocharski, M.[Maciej], Østergaard, L.R.[Lasse Riis], Initiative, T.A.D.N.[The Alzheimers Disease Neuroimaging],
Sulcal and Cortical Features for Classification of Alzheimer's Disease and Mild Cognitive Impairment,
SCIA19(427-438).
Springer DOI 1906
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Gorodissky, O.A., Sharon, A., Danov, A., Friedman, A., Raviv, T.R.,
Symmetry-Based Analysis of Diffusion MRI for the Detection of Brain Impairments,
ICIP18(376-379)
IEEE DOI 1809
Isosurfaces, Diffusion tensor imaging, Brain injuries, Symmetry, Shape dissimilarity, MRI, Fractional Anisotropy, Mean Diffusion, Contact Sport players BibRef

Pan, X., Adel, M., Fossati, C., Gaidon, T., Guedj, E.,
Alzheimer's Disease Diagnosis with FDG-PET Brain Images By Using Multi-Level Features,
ICIP18(366-370)
IEEE DOI 1809
Feature extraction, Dementia, Magnetic resonance imaging, Positron emission tomography, Symmetric matrices, Alzheimer's Disease BibRef

Zuanon, R.[Rachel], de Faria, B.A.C.[Barbara Alves Cardoso],
Landscape Design and Neuroscience Cooperation: Contributions to the Non-pharmacological Treatment of Alzheimer's Disease,
DHM18(353-374).
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Ben-Ahmed, O., Lecellier, F., Paccalin, M., Fernandez-Maloigne, C.,
Multi-View Visual Saliency-Based MRI Classification for Alzheimer's Disease Diagnosis,
IPTA17(1-6)
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biomedical MRI, brain, diseases, image classification, learning (artificial intelligence), medical image processing, visual saliency BibRef

Lazli, L., Boukadoum, M., Aït-Mohamed, O.,
Brain Tissue Classification of Alzheimer Disease Using Partial Volume Possibilistic Modeling: Application to ADNI Phantom Images,
IPTA17(1-5)
IEEE DOI 1804
biological tissues, biomedical MRI, brain, diseases, fuzzy set theory, image classification, image denoising, Possibilistic c-means algorithm BibRef

Li, Q.[Qing], Wu, X.[Xia], Xu, L.[Lele], Yao, L.[Li], Chen, K.W.[Ke-Wei],
Multi-Feature Kernel Discriminant Dictionary Learning for Classification in Alzheimer's Disease,
DICTA17(1-6)
IEEE DOI 1804
biomedical MRI, diseases, face recognition, feature extraction, image classification, medical image processing, Training
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Mehta, R., Chakraborty, R., Singh, V., Xiong, Y.,
Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains,
ICCV19(10570-10578)
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Tensile stress, Manifolds, Computational modeling, Brain modeling, Data models, Solid modeling, Three-dimensional displays BibRef

Chakraborty, R., Vemuri, B.C.,
Statistical analysis of longitudinal data and applications to neuro-imaging,
ICIP17(211-214)
IEEE DOI 1803
Dementia, Electronics packaging, Frequency modulation, Manifolds, Measurement, Statistical analysis, Trajectory, Trajectories BibRef

El-Gamal, F.E.Z.A., Elmogy, M.M., Atwan, A., Ghazal, M., Barnes, G.N., Hajjdiab, H., Keynton, R., El-Baz, A.S.,
Significant Region-Based Framework for Early Diagnosis of Alzheimer's Disease Using11C PiB-PET Scans,
ICPR18(2989-2994)
IEEE DOI 1812
Diseases, Feature extraction, Labeling, Statistical analysis, Support vector machines, Positron emission tomography, Standardization BibRef

El-Gamal, F.E.Z.A., Elmogy, M.M., Ghazal, M., Atwan, A., Barnes, G.N., Casanova, M.F., Keynton, R., El-Baz, A.S.,
A novel CAD system for local and global early diagnosis of Alzheimer's disease based on PIB-PET scans,
ICIP17(3270-3274)
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Brain, Databases, Diseases, Feature extraction, Noise reduction, Probabilistic logic, Support vector machines, PIB-PET BibRef

Shams-Baboli, A., Ezoji, M.,
A Zernike moment based method for classification of Alzheimer's disease from structural MRI,
IPRIA17(38-43)
IEEE DOI 1712
backpropagation, biomedical MRI, diseases, feature extraction, image classification, medical image processing, neural nets, mild cognitive impairment BibRef

Konukoglu, E.[Ender], Glocker, B.[Ben],
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Endo, T., Ukita, N., Tanaka, H., Hagita, N., Nakamura, S., Adachi, H., Ikeda, M., Kazui, H., Kudo, T.,
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MVA17(262-265)
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Ding, J.W.[Jun-Wei], Huang, Q.[Qiu],
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ICIVC17(660-664)
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Diseases, Hippocampus, Image reconstruction, Magnetic resonance imaging, Prognostics and health management, Proteins, Support vector machines, alzheimers, image processing, manifold, medical image, prediction BibRef

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SSSPR18(449-459).
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Earlier:
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Kumar, K., Desrosiers, C., Chaddad, A., Toews, M.,
Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers,
ICPR16(2162-2167)
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Alzheimer's disease, Biomarkers, Brain modeling, Data models, Databases, Neuroimaging BibRef

Montenegro, J.M.F.[Juan Manuel Fernandez], Villarini, B.[Barbara], Gkelias, A.[Athanasios], Argyriou, V.[Vasileios],
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Montenegro, J.M.F.[Juan Manuel Fernandez], Gkelias, A.[Athanasios], Argyriou, V.[Vasileios],
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Rudas, J.[Jorge], Martínez, D.[Darwin], Demertzi, A.[Athena], Di Perri, C.[Carol], Heine, L.[Lizette], Tshibanda, L.[Luaba], Soddu, A.[Andrea],
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Bhatkoti, P., Paul, M.,
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Alzheimer's disease BibRef

Joshi, S.H.[Shantanu H.], Xie, Q.[Qian], Kurtek, S.[Sebastian], Srivastava, A.[Anuj], Laga, H.[Hamid],
Surface Shape Morphometry for Hippocampal Modeling in Alzheimer's Disease,
DICTA16(1-8)
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Diseases BibRef

Aderghal, K.[Karim], Boissenin, M.[Manuel], Benois-Pineau, J.[Jenny], Catheline, G.[Gwenaëlle], Afdel, K.[Karim],
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CRV16(358-361)
IEEE DOI 1612
Alzheimer's disease BibRef

Kim, W.H.[Won Hwa], Kim, H.W.J.[Hyun-Woo J.], Adluru, N.[Nagesh], Singh, V.[Vikas],
Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP),
CVPR16(2443-2451)
IEEE DOI 1612
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Yang, X.[Xi], Jin, Y.[Yan], Chen, X.B.[Xiao-Bo], Zhang, H.[Han], Li, G.[Gang], Shen, D.G.[Ding-Gang],
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MLMI16(1-9).
Springer DOI 1611
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Hosseini-Asl, E., Keynton, R., El-Baz, A.,
Alzheimer's disease diagnostics by adaptation of 3D convolutional network,
ICIP16(126-130)
IEEE DOI 1610
Convolution BibRef

Rabeh, A.B., Benzarti, F., Amiri, H.,
Diagnosis of Alzheimer Diseases in Early Step Using SVM (Support Vector Machine),
CGiV16(364-367)
IEEE DOI 1608
diseases BibRef

Daianu, M.[Madelaine], Steeg, G.V.[Greg Ver], Mezher, A.[Adam], Jahanshad, N.[Neda], Nir, T.M.[Talia M.], Yan, X.[Xiaoran], Prasad, G.[Gautam], Lerman, K.[Kristina], Galstyan, A.[Aram], Thompson, P.M.[Paul M.],
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MCV15(13-23).
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Ben Ahmed, O.[Olfa], Benois-Pineau, J.[Jenny], Ben Amar, C.[Chokri], Aliara, M.[Michele], Catheline, G.[Gwenaelle],
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ICIP15(2840-2844)
IEEE DOI 1512
AD-related signature BibRef

Garg, S.[Saurabh], Tang, L.[Lisa], Traboulsee, A.[Anthony], Tam, R.[Roger],
A sensitive and efficient method for measuring change in cortical thickness using fuzzy correspondence in Alzheimer's disease,
ICIP15(3014-3018)
IEEE DOI 1512
Cortical Thickness; atrophy; gray matter; longitudinal measurement BibRef

Garali, I.[Imene], Adel, M.[Mouloud], Bourennane, S.[Salah], Guedj, E.[Eric],
Region-based brain selection and classification on pet images for Alzheimer's disease computer aided diagnosis,
ICIP15(1473-1477)
IEEE DOI 1512
Alzheimer's Disease (AD) BibRef

Moetesum, M.[Momina], Siddiqi, I.[Imran], Masroor, U.[Uzma], Djeddi, C.[Chawki],
Automated scoring of Bender Gestalt Test using image analysis techniques,
ICDAR15(666-670)
IEEE DOI 1511
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Lei, B.Y.[Bai-Ying], Chen, S.P.[Si-Ping], Ni, D.[Dong], Wang, T.F.[Tian-Fu],
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MLMI15(330-337).
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Guerrero, R., Ledig, C., Schmidt-Richberg, A., Rueckert, D.,
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MLMI15(178-185).
Springer DOI 1511
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Amoroso, N.[Nicola], Tangaro, S.[Sabina], Errico, R.[Rosangela], Garuccio, E.[Elena], Monda, A.[Anna], Sensi, F.[Francesco], Tateo, A.[Andrea], Bellotti, R.[Roberto], Initiative, T.A.D.N.[The Alzheimers Disease Neuroimaging],
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Vanderweyen, D.[Davy], Munsell, B.C.[Brent C.], Mintzer, J.E.[Jacobo E.], Mintzer, O.[Olga], Gajadharr, A.[Andy], Zhu, X.[Xun], Wu, G.R.[Guo-Rong], Joseph, J.[Jane], Initiative, T.A.D.N.[The Alzheimers Disease Neuroimaging],
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Huang, L.[Lei], Gao, Y.Z.[Yao-Zong], Jin, Y.[Yan], Thung, K.H.[Kim-Han], Shen, D.G.[Ding-Gang],
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MLMI15(246-254).
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Andersen, S.K.[Simon Kragh], Jakobsen, C.E.[Christian Elmholt], Pedersen, C.H.[Claus Hougaard], Rasmussen, A.M.[Anders Munk], Plocharski, M.[Maciej], Østergaard, L.R.[Lasse Riis],
Classification of Alzheimer's Disease from MRI Using Sulcal Morphology,
SCIA15(103-113).
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Zhao, Y.[Yilu], He, L.H.[Liang-Hua],
Deep Learning in the EEG Diagnosis of Alzheimer's Disease,
DeepLearnV14(340-353).
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Aidos, H.[Helena], Duarte, J.[Joao], Fred, A.[Ana],
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ICIP14(21-25)
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Dementia-Related Features in Longitudinal MRI: Tracking Keypoints over Time,
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Díaz, G.[Gloria], García-Polo, P.[Pablo], Mato, V.[Virginia], Alfayate, E.[Eva], Hernández-Tamames, J.A.[Juan Antonio], Malpica, N.[Norberto],
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CIARP14(714-721).
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Li, F.[Feng], Tran, L.[Loc], Thung, K.H.[Kim-Han], Ji, S.W.[Shui-Wang], Shen, D.G.[Ding-Gang], Li, J.[Jiang],
Robust Deep Learning for Improved Classification of AD/MCI Patients,
MLMI14(240-247).
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MLMI14(77-84).
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Hu, C.H.[Chen-Hui], Hua, X.[Xue], Thompson, P.M.[Paul M.], El Fakhri, G.[Georges], Li, Q.Z.[Quan-Zheng],
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MLMI14(42-49).
Springer DOI 1410
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Bron, E.[Esther], Smits, M.[Marion], van Swieten, J.[John], Niessen, W.[Wiro], Klein, S.[Stefan],
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MLMI14(272-279).
Springer DOI 1410
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Shi, Y.H.[Ying-Huan], Suk, H.I.[Heung-Il], Gao, Y.[Yang], Shen, D.G.[Ding-Gang],
Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis,
CVPR14(2721-2728)
IEEE DOI 1409
Alzheimer and Mild Cognitive Impairment. BibRef

Zhu, X.F.[Xiao-Feng], Suk, H.I.[Heung-Il], Shen, D.G.[Ding-Gang],
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CVPR14(3089-3096)
IEEE DOI 1409
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Yan, Z.N.[Zhen-Nan], Zhang, S.T.[Shao-Ting], Liu, X.F.[Xiao-Feng], Metaxas, D.N.[Dimitris N.], Montillo, A.[Albert],
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Liu, S.[Sidong], Zhang, L.[Lelin], Cai, W.D.[Wei-Dong], Song, Y.[Yang], Wang, Z.Y.[Zhi-Yong], Wen, L.F.[Ling-Feng], Feng, D.D.[David Dagan],
A supervised multiview spectral embedding method for neuroimaging classification,
ICIP13(601-605)
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Alzheimer's disease BibRef

Simoes, R.[Rita], Slump, C.[Cornelis], van Cappellen-van Walsum, A.M.[Anne-Marie],
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Liu, S.[Sidong], Cai, W.D.[Wei-Dong], Wen, L.F.[Ling-Feng], Feng, D.D.[David Dagan],
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IEEE DOI 1302
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Sun, Z.[Zhuo], Jasinschi, R.S.[Radu S.], Veerman, J.A.C.[Jan A.C.],
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ICIP14(3503-3507)
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ICIP12(1253-1256).
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Gomez, F., Soddu, A., Noirhomme, Q., Vanhaudenhuyse, A., Tshibanda, L., Lepore, N., Laureys, S.,
DTI based structural damage characterization for Disorders of Consciousness,
ICIP12(1257-1260).
IEEE DOI 1302
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ICIP12(1237-1240).
IEEE DOI 1302
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ICIP12(1241-1244).
IEEE DOI 1302
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MBIA12(41-50).
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Springer DOI 1210
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CIARP12(559-566).
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IEEE DOI 1208
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Veerman, J.A.C., Soldea, O., Sahindrakar, P., Wan, Y., Jasinschi, R.S.,
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ICIP11(1593-1596).
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JointMMCC: Joint Maximum-Margin Classification and Clustering of Imaging Data,
MedImg(31), No. 5, May 2012, pp. 1124-1140.
IEEE DOI 1202
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Multi-Kernel Classification for Integration of Clinical and Imaging Data: Application to Prediction of Cognitive Decline in Older Adults,
MLMI11(26-34).
Springer DOI 1109
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Gray, K.R.[Katherine R.], Aljabar, P.[Paul], Heckemann, R.A.[Rolf A.], Hammers, A.[Alexander], Rueckert, D.[Daniel],
Random Forest-Based Manifold Learning for Classification of Imaging Data in Dementia,
MLMI11(159-166).
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Long, X.J.[Xiao-Jing], Wyatt, C.[Chris],
An automatic unsupervised classification of MR images in Alzheimer's disease,
CVPR10(2910-2917).
IEEE DOI 1006
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Silveira, M.[Margarida], Marques, J.S.[Jorge S.],
Boosting Alzheimer Disease Diagnosis Using PET Images,
ICPR10(2556-2559).
IEEE DOI 1008
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ICPR10(2720-2723).
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Akgul, C.B.[Ceyhun Burak], Ekin, A.[Ahmet],
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ICPR10(265-268).
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Akgül, C.B.[Ceyhun Burak], Ünay, D.[Devrim], Ekin, A.[Ahmet],
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ICPR10(2592-2595).
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Hu, Z.H.[Zheng-Hui], Shi, P.C.[Peng-Cheng],
Regularity and Complexity of Human Electroencephalogram Dynamics: Applications to Diagnosis of Alzheimers Disease,
ICPR06(III: 245-248).
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