20.9.2 Brain, Cortex, Alzheimer's Disease, Dementia

Chapter Contents (Back)
Includes other mental problems. Brain. Cortex. Alzheimer's Disease. See also Brain, Cortex, Registration, Alignment, MRI, Other. See also Functional Magnetic Resonance, fMRI.

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
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Freeborough, P.A.[Peter A.],
A Comparison of Fractal Texture Descriptors,
BMVC97(xx-yy).
HTML Version. 0209
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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
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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.,
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 for the Alzheimer's Disease Neuroimaging Initiative, 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.
WWW 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: The Alzheimer's Disease Neuroimaging Initiative,
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: The Alzheimer's Disease Neuroimaging Initiative.
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

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.[Xuanfeng], 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


Endo, T., Ukita, N., Tanaka, H., Hagita, N., Nakamura, S., Adachi, H., Ikeda, M., Kazui, H., Kudo, T.,
Initial response time measurement in eye movement for dementia screening test,
MVA17(262-265)
DOI Link 1708
Cameras, Dementia, Facial features, Feature extraction, Iris, Time, factors BibRef

Ding, J.[Junwei], Huang, Q.[Qiu],
Prediction of MCI to AD conversion using Laplace Eigenmaps learned from FDG and MRI images of AD patients and healthy controls,
ICIVC17(660-664)
IEEE DOI 1708
Diseases, Hippocampus, Image reconstruction, Magnetic resonance imaging, Prognostics and health management, Proteins, Support vector machines, alzheimers, image processing, manifold, medical image, prediction BibRef

Shukla, P., Gupta, T., Saini, A., Singh, P., Balasubramanian, R.,
A Deep Learning Frame-Work for Recognizing Developmental Disorders,
WACV17(705-714)
IEEE DOI 1609
Atmospheric modeling, Autism, Computer vision, Face, Genetics, Neural networks, Support, vector, machines BibRef

Wang, J.[Jianjia], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Detecting Alzheimer's Disease Using Directed Graphs,
GbRPR17(94-104).
Springer DOI 1706
BibRef

Bernardes, R.[Rui], Silva, G.[Gilberto], Chiquita, S.[Samuel], Serranho, P.[Pedro], Ambrósio, A.F.[António Francisco],
Retinal Biomarkers of Alzheimer's Disease: Insights from Transgenic Mouse Models,
ICIAR17(541-550).
Springer DOI 1706
BibRef

Kumar, K., Desrosiers, C., Chaddad, A., Toews, M.,
Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers,
ICPR16(2162-2167)
IEEE DOI 1705
Alzheimer's disease, Biomarkers, Brain modeling, Data models, Databases, Neuroimaging BibRef

Montenegro, J.M.F.[Juan Manuel Fernandez], Gkelias, A.[Athanasios], Argyriou, V.[Vasileios],
Emotion Understanding Using Multimodal Information Based on Autobiographical Memories for Alzheimer's Patients,
Assist16(I: 252-268).
Springer DOI 1704
BibRef

Cury, C.[Claire], Lorenzi, M.[Marco], Cash, D.[David], Nicholas, J.M.[Jennifer M.], Routier, A.[Alexandre], Rohrer, J.[Jonathan], Ourselin, S.[Sebastien], Durrleman, S.[Stanley], Modat, M.[Marc],
Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of Early Changes in Neurodegenerative Disease,
SeSAME16(63-75).
Springer DOI 1703
BibRef

Rudas, J.[Jorge], Martínez, D.[Darwin], Demertzi, A.[Athena], Di Perri, C.[Carol], Heine, L.[Lizette], Tshibanda, L.[Luaba], Soddu, A.[Andrea],
Multivariate Functional Network Connectivity for Disorders of Consciousness,
CIARP16(434-442).
Springer DOI 1703
BibRef

Shakeri, M.[Mahsa], Lombaert, H.[Herve], Tripathi, S.[Shashank], Kadoury, S.[Samuel],
Deep Spectral-Based Shape Features for Alzheimer's Disease Classification,
SeSAME16(15-24).
Springer DOI 1703
BibRef

Bhatkoti, P., Paul, M.,
Early diagnosis of Alzheimer's disease: A multi-class deep learning framework with modified k-sparse autoencoder classification,
ICVNZ16(1-5)
IEEE DOI 1701
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)
IEEE DOI 1701
Diseases BibRef

Aderghal, K.[Karim], Boissenin, M.[Manuel], Benois-Pineau, J.[Jenny], Catheline, G.[Gwenaëlle], Afdel, K.[Karim],
Classification of sMRI for AD Diagnosis with Convolutional Neuronal Networks: A Pilot 2-D+ epsilon Study on ADNI,
MMMod17(I: 690-701).
Springer DOI 1701
BibRef

Tang, X., Albert, M., Miller, M.I.[Michael I.], Younes, L.[Laurent],
Change Point Estimation of the Hippocampal Volumes in Alzheimer's Disease,
CRV16(358-361)
IEEE DOI 1612
Alzheimer's disease BibRef

Kim, W.H.[Won Hwa], Kim, H.J.[Hyunwoo 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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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
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ICIP15(2840-2844)
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ICIP15(3014-3018)
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Region-based brain selection and classification on pet images for Alzheimer's disease computer aided diagnosis,
ICIP15(1473-1477)
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Automated scoring of Bender Gestalt Test using image analysis techniques,
ICDAR15(666-670)
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Springer DOI 1511
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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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MLMI14(240-247).
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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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MCV13(65-73).
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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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IEEE DOI 1302
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ICIP14(3503-3507)
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ICIP12(1253-1256).
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ICIP12(1257-1260).
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ICIP12(1237-1240).
IEEE DOI 1302
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ICIP12(1241-1244).
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Springer DOI 1210
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CIARP12(559-566).
Springer DOI 1209
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CVPR12(940-947).
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MLMI11(159-166).
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ICPR10(265-268).
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ICPR06(III: 245-248).
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