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.J.[Wen-Ji], 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.M.,
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

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

Zhu, W.Y.[Wen-Yong], Sun, L.[Liang], Huang, J.S.[Jia-Shuang], Han, L.X.[Liang-Xiu], Zhang, D.Q.[Dao-Qiang],
Dual Attention Multi-Instance Deep Learning for Alzheimer's Disease Diagnosis With Structural MRI,
MedImg(40), No. 9, September 2021, pp. 2354-2366.
IEEE DOI 2109
Feature extraction, Diseases, Pathology, Deep learning, Medical diagnosis, Magnetic resonance imaging, Grey matter, sMRI 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

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Strength and similarity guided group-level brain functional network construction for MCI diagnosis,
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Elsevier DOI 1901
Alzheimers disease, Mild cognitive impairment, Resting-state functional magnetic resonance imaging (rs-fMRI), Diagnosis BibRef

Peng, J.L.[Jia-Lin], Zhu, X.F.[Xiao-Feng], Wang, Y.[Ye], An, L.[Le], Shen, D.G.[Ding-Gang],
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Elsevier DOI 1901
Structured sparsity, Multimodal features, Multiple kernel learning, Feature selection, Alzheimer's disease diagnosis BibRef

Zhu, Y., Zhu, X., Kim, M., Yan, J., Kaufer, D., Wu, G.,
Dynamic Hyper-Graph Inference Framework for Computer-Assisted Diagnosis of Neurodegenerative Diseases,
MedImg(38), No. 2, February 2019, pp. 608-616.
IEEE DOI 1902
Imaging, Training, Testing, Diseases, Data models, Training data, Neuroimaging, Hyper-graph learning, computer assisted diagnosis, neurodegenerative disease BibRef

Singh, S.[Sneha], Anand, R.S.,
Multimodal neurological image fusion based on adaptive biological inspired neural model in nonsubsampled Shearlet domain,
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Neuroimaging computer-aided diagnosis systems for Alzheimer's disease,
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Scan-Time Corrections for 80-100-min Standardizetd Uptake Volume Ratios to Measure the 18F-AV-1451 Tracer for Tau Imaging,
MedImg(38), No. 3, March 2019, pp. 697-709.
IEEE DOI 1903
Dementia, Image reconstruction, Interpolation, Training, Magnetic resonance imaging, Tau, PET Imaging, SUVR, scan-time error, Alzheimer's disease BibRef

Yu, R.P.[Ren-Ping], Qiao, L.S.[Li-Shan], Chen, M.M.[Ming-Ming], Lee, S.W.[Seong-Whan], Fei, X.[Xuan], Shen, D.G.[Ding-Gang],
Weighted graph regularized sparse brain network construction for MCI identification,
PR(90), 2019, pp. 220-231.
Elsevier DOI 1903
Graph Laplacian regularization, Sparse representation, Brain functional network, Mild cognitive impairment (MCI) BibRef

de Stefano, C.[Claudio], Fontanella, F.[Francesco], Impedovo, D.[Donato], Pirlo, G.[Giuseppe], di Freca, A.S.[Alessandra Scotto],
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Novel Effective Connectivity Inference Using Ultra-Group Constrained Orthogonal Forward Regression and Elastic Multilayer Perceptron Classifier for MCI Identification,
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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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Chen, J., Liu, J., Calhoun, V.D.,
Translational Potential of Neuroimaging Genomic Analyses to Diagnosis and Treatment in Mental Disorders,
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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,
MedImg(38), No. 6, June 2019, pp. 1398-1408.
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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Elsevier DOI 1909
Alzheimer's disease, Maxwell-Boltzmann statistics, Network edge entropy BibRef

Zhou, T., Liu, M., Thung, K., Shen, D.,
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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Elsevier DOI 1911
Alzheimer's Disease, Multi-view, Exclusive lasso learning, Feature selection, Sparse learning BibRef

Fang, X.S.[Xu-Sheng], Liu, Z.B.[Zhen-Bing], Xu, M.C.[Ming-Chang],
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DA-PSO, dementia, hybrid swarm intelligence, MRI, OASIS BibRef

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

Kam, T., Zhang, H., Jiao, Z., Shen, D.,
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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

Lian, C.F.[Chun-Feng], Liu, M.X.[Ming-Xia], Zhang, J.[Jun], Shen, D.G.[Ding-Gang],
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI,
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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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Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer's Disease,
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IEEE DOI 2005
Alzheimer's disease, dementia, visualisation, system identification, machine learning BibRef

Zhang, Y.[Yingteng], Liu, S.[Shenquan], Yu, X.L.[Xiao-Li],
Longitudinal structural MRI analysis and classification in Alzheimer's disease and mild cognitive impairment,
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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

Koh, J.E.W.[Joel En Wei], Jahmunah, V.[Vicnesh], Pham, T.H.[The-Hanh], Oh, S.L.[Shu Lih], Ciaccio, E.J.[Edward J], Acharya, U.R.[U Rajendra], Yeong, C.H.[Chai Hong], Fabell, M.K.M.[Mohd Kamil Mohd], Rahmat, K.[Kartini], Vijayananthan, A.[Anushya], Ramli, N.[Norlisah],
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Decision support systems, Mild cognitive impairment, Diffusion-weighted imaging, Complex networks, Artificial neural networks BibRef

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CSO, dementia, ICSO, meta-heuristic, MRI BibRef

Pan, Y., Liu, M., Lian, C., Xia, Y., Shen, D.,
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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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Earlier: A1, A2, A2:
Learning Distributions of Shape Trajectories from Longitudinal Datasets: A Hierarchical Model on a Manifold of Diffeomorphisms,
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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IEEE DOI 2011
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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IEEE DOI 2012
Dielectric measurement, Dielectrics, Brain modeling, Radio frequency, Sensors, Computational modeling, Permittivity, radio frequency BibRef

Li, Y., Liu, J., Tang, Z., Lei, B.,
Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification,
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IEEE DOI 2009
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
Diseases, Databases, Time series analysis, Imaging, Monitoring, Brain modeling, Task analysis, Fused sparse network (FSN), similarity network fusion (SNF) BibRef

Chen, J.Z.[Jia-Zhou], Han, G.Q.[Guo-Qiang], Cai, H.M.[Hong-Min], Yang, D.F.[De-Fu], Laurienti, P.J.[Paul J.], Styner, M.[Martin], Wu, G.R.[Guo-Rong],
Learning Common Harmonic Waves on Stiefel Manifold: A New Mathematical Approach for Brain Network Analyses,
MedImg(40), No. 1, January 2021, pp. 419-430.
IEEE DOI 2012
Manifolds, Harmonic analysis, Diseases, Laplace equations, Optimization, Neuroimaging, Algebra, Brain network, computer-assisted diagnosis BibRef

Ganotra, R.[Reema], Dora, S.[Shirin], Gupta, S.[Shailender],
Identifying brain regions contributing to Alzheimer's disease using self regulating particle swarm optimization,
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DOI Link 2102
Alzheimer's disease, gray matter, magnetic resonance imaging, particle swarm optimization, support vector machines, white matter BibRef

Lu, L.J.[Lyu-Jian], Elbeleidy, S.[Saad], Baker, L.Z.[Lauren Zoe], Wang, H.[Hua], Nie, F.P.[Fei-Ping],
Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers,
MedImg(40), No. 3, March 2021, pp. 891-904.
IEEE DOI 2103
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IEEE DOI 2008
Biomarkers, Imaging, Neuroimaging, Data models, Predictive models, Diseases, Biological system modeling, Alzheimer's disease, imaging biomarker. Neuroimaging, Dementia, Brain modeling, Biomedical imaging BibRef

Qian, M.Y.[Ming-Yue], Zhang, Z.T.[Zhao-Ting], Chen, J.C.[Jie-Chun],
Combined mixed Gaussian model with pattern recognition in the automatic diagnosis of Alzheimer's disease,
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Huang, M.Y.[Mei-Yan], Chen, X.M.[Xiu-Mei], Yu, Y.W.[Yu-Wei], Lai, H.R.[Hao-Ran], Feng, Q.J.[Qian-Jin],
Imaging Genetics Study Based on a Temporal Group Sparse Regression and Additive Model for Biomarker Detection of Alzheimer's Disease,
MedImg(40), No. 5, May 2021, pp. 1461-1473.
IEEE DOI 2105
Genetics, Diseases, Biological system modeling, Data models, Brain modeling, Biomedical imaging, Additives, Imaging genetics, single nucleotide polymorphism BibRef

Chen, Y.Y.[Yuan-Yuan], Xia, Y.[Yong],
Iterative sparse and deep learning for accurate diagnosis of Alzheimer's disease,
PR(116), 2021, pp. 107944.
Elsevier DOI 2106
Alzheimer's disease, Mild cognitive impairment, Deep learning, Sparse regression BibRef

Wang, M.L.[Mei-Ling], Shao, W.[Wei], Hao, X.K.[Xiao-Ke], Zhang, D.Q.[Dao-Qiang],
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MedImg(40), No. 6, June 2021, pp. 1673-1686.
IEEE DOI 2106
Imaging, Correlation, Diseases, Bioinformatics, Image reconstruction, Genomics, Knowledge engineering, Brain imaging genetics, Alzheimer's disease BibRef

Ning, Z.Y.[Zhen-Yuan], Xiao, Q.[Qing], Feng, Q.J.[Qian-Jin], Chen, W.F.[Wu-Fan], Zhang, Y.[Yu],
Relation-Induced Multi-Modal Shared Representation Learning for Alzheimer's Disease Diagnosis,
MedImg(40), No. 6, June 2021, pp. 1632-1645.
IEEE DOI 2106
Magnetic resonance imaging, Diseases, Training, Testing, Data models, Bidirectional control, Alzheimer's disease, Alzheimer's disease, relational regularization
See also Relation-Aware Shared Representation Learning for Cancer Prognosis Analysis with Auxiliary Clinical Variables and Incomplete Multi-Modality Data. BibRef

Lao, H.[Huan], Zhang, X.J.[Xue-Jun], Tang, Y.[Yanyan], Liang, C.[Chan],
Alzheimer's disease diagnosis based on the visual attention model and equal-distance ring shape context features,
IET-IPR(15), No. 10, 2021, pp. 2351-2362.
DOI Link 2108
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Zhang, J.[Jie], Wu, J.F.[Jian-Feng], Li, Q.[Qingyang], Caselli, R.J.[Richard J.], Thompson, P.M.[Paul M.], Ye, J.P.[Jie-Ping], Wang, Y.L.[Ya-Lin],
Multi-Resemblance Multi-Target Low-Rank Coding for Prediction of Cognitive Decline With Longitudinal Brain Images,
MedImg(40), No. 8, August 2021, pp. 2030-2041.
IEEE DOI 2108
Task analysis, Correlation, Diseases, Dictionaries, Encoding, Neuroimaging, Alzheimer's disease, Multi-task, multi-resemblance BibRef

Basheera, S.[Shaik], Ram, M.S.S.[M Satya Sai],
Deep learning based Alzheimer's disease early diagnosis using T2w segmented gray matter MRI,
IJIST(31), No. 3, 2021, pp. 1692-1710.
DOI Link 2108
Alzheimer's disease, classification, CNN, deep learning, gray matter BibRef

Jiao, Z.Q.[Zhu-Qing], Jiao, T.X.[Ting-Xuan], Zhang, J.H.[Jia-Hao], Shi, H.F.[Hai-Feng], Wu, B.[Bona], Zhang, Y.D.[Yu-Dong],
Sparse structure deep network embedding for transforming brain functional network in early mild cognitive impairment classification,
IJIST(31), No. 3, 2021, pp. 1197-1210.
DOI Link 2108
brain functional network, dilated convolutional neural network (DCNN), transform BibRef

Bharanidharan, N., Rajaguru, H.[Harikumar],
Dementia MRI image classification using transformation technique based on elephant herding optimization with Randomized Adam method for updating the hyper-parameters,
IJIST(31), No. 3, 2021, pp. 1221-1245.
DOI Link 2108
dementia, elephant herding optimization, evolutionary algorithms, MRI, swarm intelligence BibRef

Karim, R.[Razaul], Shahrior, A.[Ashef], Rahman, M.M.[Mohammad Motiur],
Machine learning-based tri-stage classification of Alzheimer's progressive neurodegenerative disease using PCA and mRMR administered textural, orientational, and spatial features,
IJIST(31), No. 4, 2021, pp. 2060-2074.
DOI Link 2112
Alzheimer's, feature extraction, machine learning, MRI, VLAD BibRef

Li, Y.[Yang], Liu, J.Y.[Jing-Yu], Jiang, Y.Q.[Yi-Qiao], Liu, Y.[Yu], Lei, B.Y.[Bai-Ying],
Virtual Adversarial Training-Based Deep Feature Aggregation Network From Dynamic Effective Connectivity for MCI Identification,
MedImg(41), No. 1, January 2022, pp. 237-251.
IEEE DOI 2201
Feature extraction, Diseases, Heuristic algorithms, Brain modeling, Time series analysis, Deep learning, Training, virtual adversarial training BibRef

Gopinath, K.[Karthik], Desrosiers, C.[Christian], Lombaert, H.[Herve],
Learnable Pooling in Graph Convolutional Networks for Brain Surface Analysis,
PAMI(44), No. 2, February 2022, pp. 864-876.
IEEE DOI 2201
Brain, Convolution, Geometry, Task analysis, Surface treatment, Alzheimer's disease, Learnable pooling, alzheimer classification BibRef

Shakeri, M.[Mahsa], Lombaert, H.[Herve], Tripathi, S.[Shashank], Kadoury, S.[Samuel],
Deep Spectral-Based Shape Features for Alzheimer's Disease Classification,
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Springer DOI 1703
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Dinh, T.Q.[Tuan Q.], Xiong, Y.[Yunyang], Huang, Z.C.[Zhi-Chun], Vo, T.[Tien], Mishra, A.[Akshay], Kim, W.H.[Won Hwa], Ravi, S.N.[Sathya N.], Singh, V.[Vikas],
Performing Group Difference Testing on Graph Structured Data from GANs: Analysis and Applications in Neuroimaging,
PAMI(44), No. 2, February 2022, pp. 877-889.
IEEE DOI 2201
Is the result the same using GAN generated data and the original real data for testing. Statistical analysis, Diseases, Machine learning, Training data, Training, non-euclidean BibRef

Eroglu, Y.[Yesim], Yildirim, M.[Muhammed], Cinar, A.[Ahmet],
mRMR-based hybrid convolutional neural network model for classification of Alzheimer's disease on brain magnetic resonance images,
IJIST(32), No. 2, 2022, pp. 517-527.
DOI Link 2203
Alzheimer's disease, classification, KNN, machine learning, MRI, SVM BibRef

Babu, G.S.[G. Stalin], Rao, S.N.T.[S. N. Tirumala], Rao, R.R.[R. Rajeswara],
Automated assessment for Alzheimer's disease diagnosis from MRI images: Meta-heuristic assisted deep learning model,
IJIST(32), No. 2, 2022, pp. 544-563.
DOI Link 2203
Alzheimer disease, CG-DU algorithm, DCNN, geometric Haralick, gray wolf optimizer BibRef

Dora, L.[Lingraj], Agrawal, S.[Sanjay], Panda, R.[Rutuparna], Abraham, A.[Ajith],
An efficient multiclass classifier for classification of Alzheimer's disease/mild cognitive impairment/Normal subjects,
IJIST(32), No. 2, 2022, pp. 629-641.
DOI Link 2203
disease classification, hybrid particle swarm optimization-squirrel search algorithm, ternary classifier BibRef

Pohl, T.[Tomáš], Jakab, M.[Marek], Benesova, W.[Wanda],
Interpretability of deep neural networks used for the diagnosis of Alzheimer's disease,
IJIST(32), No. 2, 2022, pp. 673-686.
DOI Link 2203
Alzheimer's disease, deep neural networks, interpretability, layer-wise relevance propagation, magnetic resonance imaging BibRef

Shi, Y.[Yuang], Zu, C.[Chen], Hong, M.[Mei], Zhou, L.P.[Lu-Ping], Wang, L.[Lei], Wu, X.[Xi], Zhou, J.[Jiliu], Zhang, D.[Daoqiang], Wang, Y.[Yan],
ASMFS: Adaptive-Similarity-Based Multi-Modality Feature Selection for Classification of Alzheimer's Disease,
PR(126), 2022, pp. 108566.
Elsevier DOI 2204
Multi-modality, Similarity learning, Feature selection, Alzheimer's disease
See also Feature Selection with Kernel Class Separability. BibRef

Borovkova, M.[Mariia], Sieryi, O.[Oleksii], Lopushenko, I.[Ivan], Kartashkina, N.[Natalia], Pahnke, J.[Jens], Bykov, A.[Alexander], Meglinski, I.[Igor],
Screening of Alzheimer's Disease With Multiwavelength Stokes Polarimetry in a Mouse Model,
MedImg(41), No. 4, April 2022, pp. 977-982.
IEEE DOI 2204
Brain, Polarimetry, Mice, Measurement by laser beam, Laser beams, Microscopy, Optical microscopy, Optical polarimetry, scattering, Stokes vector BibRef

Lian, C.F.[Chun-Feng], Liu, M.X.[Ming-Xia], Pan, Y.S.[Yong-Sheng], Shen, D.G.[Ding-Gang],
Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images,
Cyber(52), No. 4, April 2022, pp. 1992-2003.
IEEE DOI 2204
Feature extraction, Brain modeling, Task analysis, Dementia, Solid modeling, Medical diagnosis, Alzheimer's disease (AD), weakly supervised localization BibRef

Perugia, G.[Giulia], Díaz-Boladeras, M.[Marta], Català-Mallofré, A.[Andreu], Barakova, E.I.[Emilia I.], Rauterberg, M.[Matthias],
ENGAGE-DEM: A Model of Engagement of People With Dementia,
AffCom(13), No. 2, April 2022, pp. 926-943.
IEEE DOI 2206
Dementia, Computational modeling, Mathematical model, Task analysis, Physiology, Games, Atmospheric measurements, social agents/robotics BibRef

Kaur, S.[Swapandeep], Gupta, S.[Sheifali], Singh, S.[Swati], Gupta, I.[Isha],
Detection of Alzheimer's Disease Using Deep Convolutional Neural Network,
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DOI Link 2206
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Shankar, V.G.[Venkatesh Gauri], Sisodia, D.S.[Dilip Singh], Chandrakar, P.[Preeti],
A novel discriminant feature selection-based mutual information extraction from MR brain images for Alzheimer's stages detection and prediction,
IJIST(32), No. 4, 2022, pp. 1172-1191.
DOI Link 2207
Alzheimer's disease, classification, feature selection, machine learning, medical imaging system, neurodegenerative disorder BibRef

Yu, L.[Lu], Xiang, W.[Wei], Fang, J.[Juan], Chen, Y.P.P.[Yi-Ping Phoebe], Zhu, R.[Ruifeng],
A novel explainable neural network for Alzheimer's disease diagnosis,
PR(131), 2022, pp. 108876.
Elsevier DOI 2208
Explainable neural networks, XAI, High-resolution heatmap, MRI BibRef

Pei, Z.[Zhao], Wan, Z.[Zhiyang], Zhang, Y.N.[Yan-Ning], Wang, M.[Miao], Leng, C.[Chengcai], Yang, Y.H.[Yee-Hong],
Multi-scale attention-based pseudo-3D convolution neural network for Alzheimer's disease diagnosis using structural MRI,
PR(131), 2022, pp. 108825.
Elsevier DOI 2208
Diagnosis of Alzheimer's disease, Pseudo-3D, Attention mechanism, Multi-scale, Joint loss function BibRef

Dwivedi, S.[Shubham], Goel, T.[Tripti], Tanveer, M., Murugan, R., Sharma, R.[Rahul],
Multimodal Fusion-Based Deep Learning Network for Effective Diagnosis of Alzheimer's Disease,
MultMedMag(29), No. 2, April 2022, pp. 45-55.
IEEE DOI 2208
Magnetic resonance imaging, Alzheimer's disease, Feature extraction, Atrophy, Computational modeling, Positron emission tomography (PET) BibRef

Maronnat, F.[Florian], Seguin, M.[Margaux], Djemal, K.[Khalifa],
Cognitive tasks modelization and description in VR environment for Alzheimer's disease state identification,
IPTA20(1-7)
IEEE DOI 2206
Solid modeling, Biological system modeling, Virtual environments, Tools, Task analysis, Statistics, Diseases, Alzheimer disease, dementia BibRef

Bastos, J.[José], Silva, F.[Filipe], Georgieva, P.[Petia],
Deep Learning for Diagnosis of Alzheimer's Disease with FDG-PET Neuroimaging,
IbPRIA22(95-107).
Springer DOI 2205
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Parziale, A.[Antonio], Cioppa, A.D.[Antonio Della], Marcelli, A.[Angelo],
Investigating One-Class Classifiers to Diagnose Alzheimer's Disease from Handwriting,
CIAP22(I:111-123).
Springer DOI 2205
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Fontanella, F.[Francesco], Pinelli, S.[Sonia], Babiloni, C.[Claudio], Lizio, R.[Roberta], del Percio, C.[Claudio], Lopez, S.[Susanna], Noce, G.[Giuseppe], Giubilei, F.[Franco], Stocchi, F.[Fabrizio], Frisoni, G.B.[Giovanni B.], Nobili, F.[Flavio], Ferri, R.[Raffaele], D'Alessandro, T.[Tiziana], Cilia, N.D.[Nicole Dalia], de Stefano, C.[Claudio],
Machine Learning to Predict Cognitive Decline of Patients with Alzheimer's Disease Using EEG Markers: A Preliminary Study,
CIAP22(I:137-147).
Springer DOI 2205
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Detection of Cortical Demantia in MRI Images Using Convolutional Autoencoder Neural Network,
IPRIA21(1-3)
IEEE DOI 2201
Image analysis, Magnetic resonance imaging, Brain mapping, Pattern recognition, Convolutional neural networks, Classification BibRef

Ayyar, M.P.[Meghna P.], Benois-Pineau, J.[Jenny], Zemmari, A.[Akka], Catheline, G.[Gwenaelle],
Explaining 3D CNNs for Alzheimer's Disease Classification on sMRI Images with Multiple ROIs,
ICIP21(284-288)
IEEE DOI 2201
Heating systems, Deep learning, Correlation, Magnetic resonance imaging, Image processing, Deep Learning understanding BibRef

Liu, C.[Chao], Yang, X.D.[Xiao-Dong], Chong, D.[Dading], Wang, W.[Wenwu], Li, L.[Liang],
Enhancing Alzheimer's Disease Diagnosis via Hierarchical 3D-FCN with Multi-Modal Features,
ICIP21(304-308)
IEEE DOI 2201
Training, Neurological diseases, Sociology, Senior citizens, Feature extraction, Medical diagnosis, Multi-layer perceptron BibRef

Zhang, L.[Lin], Xin, B.[Bowen], Yan, S.Z.[Shao-Zhen], Zheng, C.[Chaoiie], Zhou, Y.[Yun], Lu, J.[Jie], Wang, X.Y.[Xiu-Ying],
Multi-stratification feature selection for diagnostic analysis of Alzheimer's disease,
DICTA21(01-07)
IEEE DOI 2201
Neuroimaging, Brain, Magnetic resonance imaging, Digital images, Feature extraction, Alzheimer's disease, Task analysis BibRef

Cilia, N.D.[Nicole Dalia], de Stefano, C.[Claudio], Marrocco, C.[Claudio], Fontanella, F.[Francesco], Molinara, M.[Mario], di Freca, A.S.[Alessandra Scotto],
Deep Transfer Learning for Alzheimer's disease detection,
ICPR21(9904-9911)
IEEE DOI 2105
Decision support systems, Shape, Image color analysis, Transfer learning, Neural networks, Feature extraction, Pattern recognition BibRef

Yu, F.[Fei], Zhao, B.Q.[Bao-Qi], Ge, Q.Q.[Qing-Qing], Zhang, Z.J.[Zhi-Jie], Sun, J.M.[Jun-Mei], Li, X.M.[Xiu-Mei],
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AIHA20(575-586).
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AIHA20(536-545).
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Cilia, N.D.[Nicole Dalia], de Stefano, C.[Claudio], Fontanella, F.[Francesco], di Freca, A.S.[Alessandra Scotto],
Handwriting-based Classifier Combination for Cognitive Impairment Prediction,
AIHA20(587-599).
Springer DOI 2103
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AIHA20(559-574).
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Introducing Transfer Leaming to 3D ResNet-18 for Alzheimer's Disease Detection on MRI Images,
IVCNZ20(1-6)
IEEE DOI 2012
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Matrix Classifier On Dynamic Functional Connectivity For Mci Identification,
ICIP20(325-329)
IEEE DOI 2011
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)
IEEE DOI 2008
Results on Alzheimer's. Shape, Kernel, Mathematical model, Shape measurement, Atmospheric measurements 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)
IEEE DOI 2004
Videos, Skin, Biomedical monitoring, Heart rate, Databases, Blood, Physiology, physiological signals, PIMD, deep learning, LSTM, rPPG, video cameras BibRef

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MBIA19(84-92).
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MBIA19(30-38).
Springer DOI 1912
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MBIA19(139-148).
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Generative Aging of Brain MR-Images and Prediction of Alzheimer Progression,
GCPR19(247-260).
Springer DOI 1911
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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).
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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).
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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).
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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)
IEEE DOI 1804
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
See also Multi-Feature Kernel Discriminant Dictionary Learning for Face Recognition. BibRef

Mehta, R., Chakraborty, R., Singh, V., Xiong, Y.,
Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains,
ICCV19(10570-10578)
IEEE DOI 2004
Tensile stress, Manifolds, Computational modeling, Brain modeling, Data models, Solid modeling 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)
IEEE DOI 1803
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],
Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases,
MCV16(3-13).
Springer DOI 1711
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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.W.[Jun-Wei], 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

Wang, J.J.[Jian-Jia], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Quantum Edge Entropy for Alzheimer's Disease Analysis,
SSSPR18(449-459).
Springer DOI 1810
BibRef
Earlier:
Detecting Alzheimer's Disease Using Directed Graphs,
GbRPR17(94-104).
Springer DOI 1706

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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
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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)
IEEE DOI 1705
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],
Cognitive Behaviour Analysis Based on Facial Information Using Depth Sensors,
UHA3DS16(15-28).
Springer DOI 1806
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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
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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
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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],
Multivariate Functional Network Connectivity for Disorders of Consciousness,
CIARP16(434-442).
Springer DOI 1703
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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
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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.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(246-253).
Springer DOI 1611
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Zheng, X.[Xiao], Shi, J.[Jun], Ying, S.[Shihui], Zhang, Q.[Qi], Li, Y.[Yan],
Improving Single-Modal Neuroimaging Based Diagnosis of Brain Disorders via Boosted Privileged Information Learning Framework,
MLMI16(95-103).
Springer DOI 1611
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Zu, C.[Chen], Gao, Y.[Yue], Munsell, B.[Brent], Kim, M.J.[Min-Jeong], Peng, Z.[Ziwen], Zhu, Y.Y.[Ying-Ying], Gao, W.[Wei], Zhang, D.Q.[Dao-Qiang], Shen, D.G.[Ding-Gang], Wu, G.R.[Guo-Rong],
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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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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
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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)
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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
Drawing tests for early detection of psychological and neurological impairments BibRef

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).
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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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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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MLMI14(77-84).
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CVPR14(2721-2728)
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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)
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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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ICIP13(601-605)
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Alzheimer's disease BibRef

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ICIP14(3503-3507)
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Alzheimer's disease BibRef
Earlier: A1, A3, A2:
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ICIP12(1253-1256).
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