21.9.2.1 Brain, Cortex, Alzheimer's Disease

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

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., 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

Li, Z., Suk, H.I., Shen, D.G., 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

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

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,
PR(88), 2019, pp. 421-430.
Elsevier DOI 1901
Alzheimers disease, Mild cognitive impairment, Resting-state functional magnetic resonance imaging (rs-fMRI), Diagnosis 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

Peng, J.L.[Jia-Lin], Zhu, X.F.[Xiao-Feng], Wang, Y.[Ye], An, L.[Le], Shen, D.G.[Ding-Gang],
Structured sparsity regularized multiple kernel learning for Alzheimer's disease diagnosis,
PR(88), 2019, pp. 370-382.
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,
IJIST(29), No. 1, March 2019, pp. 50-64.
WWW Link. 1902
BibRef

Karami, V.[Vania], Nittari, G.[Giulio], Amenta, F.[Francesco],
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Magnetic resonance imaging, Feature extraction, Genetics, Neuroimaging, Alzheimer's disease, Positron emission tomography, latent representation space BibRef

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Alzheimer's Disease, Multi-view, Exclusive lasso learning, Feature selection, Sparse learning BibRef

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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

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Alzheimer's disease, dementia, visualisation, system identification, machine learning 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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Shape, Trajectory, Computational modeling, Manifolds, Spatiotemporal phenomena, Data models, Numerical models BibRef

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Dielectric measurement, Dielectrics, Brain modeling, Radio frequency, Sensors, Computational modeling, Permittivity, radio frequency BibRef

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Manifolds, Harmonic analysis, Diseases, Laplace equations, Optimization, Neuroimaging, Algebra, Brain network, computer-assisted diagnosis BibRef

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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],
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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],
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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],
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Alzheimer's disease, Mild cognitive impairment, Deep learning, Sparse regression BibRef

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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],
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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
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Lao, H.[Huan], Zhang, X.J.[Xue-Jun], Tang, Y.Y.[Yan-Yan], Liang, C.[Chan],
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Zhang, J.[Jie], Wu, J.F.[Jian-Feng], Li, Q.Y.[Qing-Yang], 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,
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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,
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Alzheimer's disease, classification, CNN, deep learning, gray matter 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

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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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,
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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.Q.[Dao-Qiang], 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

Kaur, S.[Swapandeep], Gupta, S.[Sheifali], Singh, S.[Swati], Gupta, I.[Isha],
Detection of Alzheimer's Disease Using Deep Convolutional Neural Network,
IJIG(22), No. 3 2022, pp. 2140012.
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.F.[Rui-Feng],
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

Jung, E.[Euijin], Luna, M.[Miguel], Park, S.H.[Sang Hyun],
Conditional GAN with 3D discriminator for MRI generation of Alzheimer's disease progression,
PR(133), 2023, pp. 109061.
Elsevier DOI 2210
Conditional GAN, Alzheimer's disease, 3D Discriminator, Magnetic resonance image generation, Adaptive identity loss BibRef

Qasim Abbas, S., Chi, L.H.[Lian-Hua], Chen, Y.P.P.[Yi-Ping Phoebe],
Transformed domain convolutional neural network for Alzheimer's disease diagnosis using structural MRI,
PR(133), 2023, pp. 109031.
Elsevier DOI 2210
Alzheimer disease (AD) detection, Brain disease, Convolutional neural network (CNN), Supervised learning, AD diagnosis BibRef

Ouyang, J.H.[Jia-Hong], Zhao, Q.Y.[Qing-Yu], Adeli, E.[Ehsan], Zaharchuk, G.[Greg], Pohl, K.M.[Kilian M.],
Disentangling Normal Aging From Severity of Disease via Weak Supervision on Longitudinal MRI,
MedImg(41), No. 10, October 2022, pp. 2558-2569.
IEEE DOI 2210
Diseases, Magnetic resonance imaging, Aging, Trajectory, Training, Aerospace electronics, Supervised learning, cognitive impairment BibRef

Lao, H.[Huan], Zhang, X.J.[Xue-Jun],
Diagnose Alzheimer's disease by combining 3D discrete wavelet transform and 3D moment invariants,
IET-IPR(16), No. 14, 2022, pp. 3948-3964.
DOI Link 2212
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Aghaei, A.[Atefe], Moghaddam, M.E.[Mohsen Ebrahimi], Malek, H.[Hamed],
Interpretable ensemble deep learning model for early detection of Alzheimer's disease using local interpretable model-agnostic explanations,
IJIST(32), No. 6, 2022, pp. 1889-1902.
DOI Link 2212
Alzheimer's disease, ensemble deep learning, Inception-V3, ResNet-50, structural MRI, transfer learning BibRef

Sharma, S.[Shallu], Mandal, P.K.[Pravat Kumar],
A Comprehensive Report on Machine Learning-Based Early Detection of Alzheimer's Disease Using Multi-Modal Neuroimaging Data,
Surveys(55), No. 2, February 2023, pp. xx-yy.
DOI Link 2212
Survey, Alzheimer's. feature scaling, feature fusion, feature selection, Alzheimer disease, machine learning algorithms, multiple modal imaging BibRef

Zheng, B.[Bowen], Gao, A.[Ang], Huang, X.N.[Xiao-Na], Li, Y.H.[Yu-Han], Liang, D.[Dong], Long, X.J.[Xiao-Jing],
A modified 3D EfficientNet for the classification of Alzheimer's disease using structural magnetic resonance images,
IET-IPR(17), No. 1, 2023, pp. 77-87.
DOI Link 2301
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Houria, L.[Latifa], Belkhamsa, N.[Noureddine], Cherfa, A.[Assia], Cherfa, Y.[Yazid],
Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines,
IJIST(33), No. 2, 2023, pp. 610-621.
DOI Link 2303
Alzheimer's disease, bag-of-feature, convolutional neural network, majority voting, support vector machine BibRef

Goenka, N.[Nitika], Tiwari, S.[Shamik],
Alzheimer's detection using various feature extraction approaches using a multimodal multi-class deep learning model,
IJIST(33), No. 2, 2023, pp. 588-609.
DOI Link 2303
18F-AV45 PET, Alzheimer's disease, multi-modality, neuroimaging biomarker, patch-based, slice-based, volumetric convnet BibRef

Oh, K.[Kwanseok], Yoon, J.S.[Jee Seok], Suk, H.I.[Heung-Il],
Learn-Explain-Reinforce: Counterfactual Reasoning and its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model,
PAMI(45), No. 4, April 2023, pp. 4843-4857.
IEEE DOI 2303
Visualization, Cognition, Brain modeling, Magnetic resonance imaging, Transforms, Perturbation methods, Alzheimer's disease BibRef

Bass, C.[Cher], da Silva, M.[Mariana], Sudre, C.[Carole], Williams, L.Z.J.[Logan Z. J.], Sousa, H.S.[Helena S.], Tudosiu, P.D.[Petru-Daniel], Alfaro-Almagro, F.[Fidel], Fitzgibbon, S.P.[Sean P.], Glasser, M.F.[Matthew F.], Smith, S.M.[Stephen M.], Robinson, E.C.[Emma C],
ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans,
MedImg(42), No. 4, April 2023, pp. 959-970.
IEEE DOI 2304
Diseases, Feature extraction, Biomedical imaging, Alzheimer's disease, Imaging, Training, Neuroimaging, Brain imaging, image-to-image translation BibRef

Zhu, Q.[Qi], Xu, B.L.[Bing-Liang], Huang, J.S.[Jia-Shuang], Wang, H.Y.[He-Yang], Xu, R.[Ruting], Shao, W.[Wei], Zhang, D.Q.[Dao-Qiang],
Deep Multi-Modal Discriminative and Interpretability Network for Alzheimer's Disease Diagnosis,
MedImg(42), No. 5, May 2023, pp. 1472-1483.
IEEE DOI 2305
Correlation, Deep learning, Brain modeling, Magnetic resonance imaging, Analytical models, Data models, Alzheimer's disease BibRef

Thushara, A., Amma, C.U.[C. Ushadevi], John, A.[Ansamma],
Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer's Disease,
IJIG(23), No. 3 2023, pp. 2240006.
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Zolfaghari, S.[Samaneh], Suravee, S.[Sumaiya], Riboni, D.[Daniele], Yordanova, K.[Kristina],
Sensor-Based Locomotion Data Mining for Supporting the Diagnosis of Neurodegenerative Disorders: A Survey,
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neurodegenerative disorders, Pervasive healthcare, cognitive decline, location data mining BibRef

Chen, H.[Hui], Guo, H.[Huiru], Xing, L.Q.[Long-Qiang], Chen, D.[Da], Yuan, T.[Ting], Zhang, Y.P.[Yun-Peng], Zhang, X.[Xuedian],
Multimodal predictive classification of Alzheimer's disease based on attention-combined fusion network: Integrated neuroimaging modalities and medical examination data,
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DOI Link 2310
Alzheimer's disease, attention mechanism, diagnosis, multi-modal, prediction BibRef

Duarte, K.T.N.[Kauê T.N.], Gobbi, D.G.[David G.], Sidhu, A.S.[Abhijot S.], McCreary, C.R.[Cheryl R.], Saad, F.[Feryal], Camicioli, R.[Richard], Smith, E.E.[Eric E.], Frayne, R.[Richard],
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PRL(175), 2023, pp. 90-94.
Elsevier DOI 2311
Image segmentation, White matter hyperintensity (WMH), Convolutional neural network (CNN), Alzheimer's disease (AD), Deep learning BibRef

Guo, Z.Q.[Zhi-Qiang], Ling, Z.H.[Zhen-Hua],
Exploring the Topics of Audio Words for Detecting Alzheimer's Disease From Spontaneous Speech,
SPLetters(30), 2023, pp. 1727-1731.
IEEE DOI 2312
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Chen, Y.Y.[Yuan-Yuan], Pan, Y.S.[Yong-Sheng], Xia, Y.[Yong], Yuan, Y.X.[Yi-Xuan],
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MedImg(42), No. 12, December 2023, pp. 3566-3578.
IEEE DOI 2312
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Lei, B.Y.[Bai-Ying], Zhu, Y.[Yun], Liang, E.[Enmin], Yang, P.[Peng], Chen, S.B.[Shao-Bin], Hu, H.[Huoyou], Xie, H.R.[Hao-Ran], Wei, Z.[Ziyi], Hao, F.[Fei], Song, X.[Xuegang], Wang, T.F.[Tian-Fu], Xiao, X.H.[Xiao-Hua], Wang, S.Q.[Shu-Qiang], Han, H.B.[Hong-Bin],
Federated Domain Adaptation via Transformer for Multi-Site Alzheimer's Disease Diagnosis,
MedImg(42), No. 12, December 2023, pp. 3651-3664.
IEEE DOI 2312
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Marcus, A.[Adam], Bentley, P.[Paul], Rueckert, D.[Daniel],
Concurrent Ischemic Lesion Age Estimation and Segmentation of CT Brain Using a Transformer-Based Network,
MedImg(42), No. 12, December 2023, pp. 3464-3473.
IEEE DOI 2312
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Tan, Y.F.[Yee-Fan], Ting, C.M.[Chee-Ming], Noman, F.[Fuad], Phan, R.C.W.[Raphaël C.W.], Ombao, H.[Hernando],
A Unified Framework for Static and Dynamic Functional Connectivity Augmentation for Multi-Domain Brain Disorder Classification,
ICIP23(635-639)
IEEE DOI 2312
BibRef

Rana, M.M.[Md Masud], Islam, M.M.[Md Manowarul], Talukder, M.A.[Md. Alamin], Uddin, M.A.[Md Ashraf], Aryal, S.I.[Sun-Il], Alotaibi, N.[Naif], Alyami, S.A.[Salem A.], Hasan, K.F.[Khondokar Fida], Moni, M.A.[Mohammad Ali],
A robust and clinically applicable deep learning model for early detection of Alzheimer's,
IET-IPR(17), No. 14, 2023, pp. 3959-3975.
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brain, cancer, diseases, tumours, deep learning BibRef

Yang, Y.W.[Yan-Wu], Ye, C.F.[Chen-Fei], Guo, X.[Xutao], Wu, T.[Tao], Xiang, Y.[Yang], Ma, T.[Ting],
Mapping Multi-Modal Brain Connectome for Brain Disorder Diagnosis via Cross-Modal Mutual Learning,
MedImg(43), No. 1, January 2024, pp. 108-121.
IEEE DOI 2401
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Shukla, A.[Amar], Tiwari, R.[Rajeev], Tiwari, S.[Shamik],
Structural biomarker-based Alzheimer's disease detection via ensemble learning techniques,
IJIST(34), No. 1, 2024, pp. e22967.
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Alzheimer disease, binary class, ensemble learning, machine learning, multiclass BibRef

Miao, S.[Shang], Xu, Q.[Qun], Li, W.M.[Wei-Min], Yang, C.[Chao], Sheng, B.[Bin], Liu, F.Y.[Fang-Yu], Bezabih, T.T.[Tsigabu T.], Yu, X.[Xiao],
MMTFN: Multi-modal multi-scale transformer fusion network for Alzheimer's disease diagnosis,
IJIST(34), No. 1, 2024, pp. e22970.
DOI Link 2401
Alzheimer's disease, attention mechanism, deep learning, multi-modal fusion, transformer BibRef

Lachinov, D.[Dmitrii], Chakravarty, A.[Arunava], Grechenig, C.[Christoph], Schmidt-Erfurth, U.[Ursula], Bogunovic, H.[Hrvoje],
Learning Spatio-Temporal Model of Disease Progression With NeuralODEs From Longitudinal Volumetric Data,
MedImg(43), No. 3, March 2024, pp. 1165-1179.
IEEE DOI 2403
Predictive models, Atrophy, Imaging, Brain modeling, Biomarkers, Solid modeling, Retina, Disease progression, deep learning, Alzheimer's disease BibRef

Wang, T.X.[Tian-Xiang], Dai, Q.[Qun],
A patch distribution-based active learning method for multiple instance Alzheimer's disease diagnosis,
PR(150), 2024, pp. 110341.
Elsevier DOI 2403
Multi-instance learning, Active learning, Alzheimer's disease, Attention mechanism BibRef

Jiang, S.Q.[Shun-Qin], Feng, Q.Y.[Qi-Yuan], Li, H.X.[Heng-Xin], Deng, Z.[Zhenyun], Jiang, Q.[Qinghong],
Attention based multi-task interpretable graph convolutional network for Alzheimer's disease analysis,
PRL(180), 2024, pp. 1-8.
Elsevier DOI 2404
Alzheimer's disease diagnosis analysis, Multi-task learning, Attention unit, Interpretability, Graph convolutional network BibRef

Illakiya, T., Karthik, R.,
A deep feature fusion network with global context and cross-dimensional dependencies for classification of mild cognitive impairment from brain MRI,
IVC(144), 2024, pp. 104967.
Elsevier DOI 2404
Mild cognitive impairment, Magnetic resonance imaging, Deep learning, Convolutional neural network, Classification: Alzheimer's disease BibRef

Chen, Z.[Zhi], Liu, Y.[Yongguo], Zhang, Y.[Yun], Zhu, J.J.[Jia-Jing], Li, Q.Q.[Qiao-Qin], Wu, X.D.[Xin-Dong],
Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer's Disease Diagnosis,
IP(33), 2024, pp. 2730-2745.
IEEE DOI 2404
Feature extraction, Task analysis, Diseases, Correlation, Neuroimaging, Data models, Manifolds, manifold learning BibRef

Xu, J.H.[Jing-Hao], Yuan, C.X.[Chen-Xi], Ma, X.C.[Xiao-Chuan], Shang, H.F.[Hui-Fang], Shi, X.S.[Xiao-Shuang], Zhu, X.F.[Xiao-Feng],
Interpretable medical deep framework by logits-constraint attention guiding graph-based multi-scale fusion for Alzheimer's disease analysis,
PR(152), 2024, pp. 110450.
Elsevier DOI Code:
WWW Link. 2405
Alzheimer's disease, Attention, Graph neural networks, Multi-scale feature fusion, Structural MRI BibRef

Han, K.[Kangfu], Li, G.[Gang], Fang, Z.W.[Zhi-Wen], Yang, F.[Feng],
Multi-Template Meta-Information Regularized Network for Alzheimer's Disease Diagnosis Using Structural MRI,
MedImg(43), No. 5, May 2024, pp. 1664-1676.
IEEE DOI 2405
Feature extraction, Metadata, Self-supervised learning, Mutual information, Alzheimer's disease, Aging, Minimization BibRef

Khojaste-Sarakhsi, M., Haghighi, S.S.[Seyedhamidreza Shahabi], Fatemi Ghomi, S.M.T., Marchiori, E.[Elena],
A 3D multi-scale CycleGAN framework for generating synthetic PETs from MRIs for Alzheimer's disease diagnosis,
IVC(146), 2024, pp. 105017.
Elsevier DOI 2405
Cycle GAN, Multi-scale GAN, 3D image-to-image translation, Image synthesis, Alzheimer's Disease diagnosis BibRef

Lei, B.[Baiying], Liang, Y.[Yu], Xie, J.Y.[Jia-Yi], Wu, Y.[You], Liang, E.[Enmin], Liu, Y.[Yong], Yang, P.[Peng], Wang, T.F.[Tian-Fu], Liu, C.[ChuanMing], Du, J.[Jichen], Xiao, X.H.[Xiao-Hua], Wang, S.Q.[Shu-Qiang],
Hybrid federated learning with brain-region attention network for multi-center Alzheimer's disease detection,
PR(153), 2024, pp. 110423.
Elsevier DOI Code:
WWW Link. 2405
Alzheimer's disease, Attention, Federated learning, Hybrid learning BibRef

Zuo, Q.[Qiankun], Wu, H.[Huisi], Chen, C.L.P.[C. L. Philip], Lei, B.[Baiying], Wang, S.Q.[Shu-Qiang],
Prior-Guided Adversarial Learning With Hypergraph for Predicting Abnormal Connections in Alzheimer's Disease,
Cyber(54), No. 6, June 2024, pp. 3652-3665.
IEEE DOI 2406
Diseases, Functional magnetic resonance imaging, Brain modeling, Feature extraction, Diffusion tensor imaging, Predictive models, prior-guided learning BibRef

Zhang, L.W.[Li-Wen], Xia, R.W.[Rong-Wei], Yang, B.Y.[Bai-Yang], Zhang, J.C.[Jin-Can], Wang, J.C.[Jin-Chan],
MSFNet-2SE: A multi-scale fusion convolutional network for Alzheimer's disease classification on magnetic resonance images,
IJIST(34), No. 4, 2024, pp. e23112.
DOI Link 2406
Alzheimer's disease, attention module, gradient centralization, multi-scale BibRef

Begum, A.P.[Afiya Parveen], Selvaraj, P.[Prabha],
Multiclass Diagnosis of Alzheimer's Disease Analysis Using Machine Learning and Deep Learning Techniques,
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Akhtar, M.[Mushir], Tanveer, M., Arshad, M.[Mohd.],
Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach,
PR(155), 2024, pp. 110637.
Elsevier DOI Code:
WWW Link. 2408
Supervised learning, Pattern classification, Loss function, Support vector machine, Twin support vector machine, Alzheimer's disease BibRef

Liu, H.R.[Hong-Rui], Gui, Y.Y.[Yuan-Yuan], Lu, H.[Hui], Liu, M.H.[Man-Hua],
A sparse transformer generation network for brain imaging genetic association,
PR(156), 2024, pp. 110845.
Elsevier DOI 2408
Imaging genetics, Sparse transformer, Alzheimer's disease, Brain aging BibRef

Hesse, L.S.[Linde S.], Dinsdale, N.K.[Nicola K.], Namburete, A.I.L.[Ana I.L.],
Prototype Learning for Explainable Brain Age Prediction,
WACV24(7888-7898)
IEEE DOI 2404
Training, Measurement, Visualization, Ultrasonic imaging, Magnetic resonance imaging, Prototypes, Predictive models, ethical computer vision BibRef

Shah, J.[Jay], Siddiquee, M.M.R.[Md Mahfuzur Rahman], Su, Y.[Yi], Wu, T.[Teresa], Li, B.X.[Bao-Xin],
Ordinal Classification with Distance Regularization for Robust Brain Age Prediction,
WACV24(7867-7876)
IEEE DOI Code:
WWW Link. 2404
Systematics, Magnetic resonance imaging, Biological system modeling, Aging, Predictive models, body pose BibRef

Kumar, S.[Suraj], Singh, N.P.[Narendra Pratap], Brahma, B.[Banalaxmi],
AI-Based Model for Detection and Classification of Alzheimer Disease,
ICCVMI23(1-6)
IEEE DOI 2403
Training, Support vector machines, Neurological diseases, Magnetic resonance imaging, Computational modeling, MRI BibRef

Huang, W.C.[Wei-Chen],
Multimodal Contrastive Learning and Tabular Attention for Automated Alzheimer's Disease Prediction,
CVAMD23(2465-2474)
IEEE DOI 2401
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Li, F.[Fanshi], Wang, Z.H.[Zhi-Hui], Guo, Y.F.[Yi-Fan], Liu, C.C.[Cong-Cong], Zhu, Y.J.[Yan-Jie], Zhou, Y.H.[Yi-Hang], Li, J.[Jun], Liang, D.[Dong], Wang, H.F.[Hai-Feng],
Dynamic Dual-Graph Fusion Convolutional Network for Alzheimer's Disease Diagnosis,
ICIP23(675-679)
IEEE DOI 2312
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Alzahrani, F.[Fatimah], Mirheidari, B.[Bahman], Blackburn, D.[Daniel], Maddock, S.[Steve], Christensen, H.[Heidi],
Investigating Visual Features for Cognitive Impairment Detection Using In-the-wild Data,
FG23(1-8)
IEEE DOI 2303
Visualization, Costs, Face recognition, Speech recognition, Gesture recognition, Feature extraction, Magnetic heads BibRef

Cilia, N.D.[Nicole Dalia], d'Alessandro, T.[Tiziana], de Stefano, C.[Claudio], Fontanella, F.[Francesco],
Offline handwriting image analysis to predict Alzheimer's disease via deep learning,
ICPR22(2807-2813)
IEEE DOI 2212
Deep learning, Motor drives, Image analysis, Shape, Image color analysis, Neural networks, Transfer learning BibRef

Wang, J.[Jianjia], Wu, C.[Chong],
Data-driven Latent Graph Structure Learning for Diagnosis of Alzheimer's Syndrome,
ICPR22(3138-3144)
IEEE DOI 2212
Image edge detection, Functional magnetic resonance imaging, Pattern recognition, Task analysis, Alzheimer's disease, Complex systems BibRef

Salih, A.[Ahmed], Galazzo, I.B.[Ilaria Boscolo], Cruciani, F.[Federica], Brusini, L.[Lorenza], Radeva, P.[Petia],
Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: A New Stability Criterion for Explainable Methods,
ICIP22(4003-4007)
IEEE DOI 2211
Stability criteria, Machine learning, Robustness, Alzheimer's disease, Monitoring, Explainability, XAI, Proxy BibRef

Bernava, G.M.[Giuseppe Massimo], Leo, M.[Marco], Carcagnì, P.[Pierluigi], Distante, C.[Cosimo],
An Advanced Tool for Semi-automatic Annotation for Early Screening of Neurodevelopmental Disorders,
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Springer DOI 2208
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Predicting Brain Degeneration with a Multimodal Siamese Neural Network,
IPTA20(1-6)
IEEE DOI 2206
Training, Recurrent neural networks, Magnetic resonance imaging, Image processing, Tools, Biological neural networks, Diseases, Alzheimer's disease 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

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Deep Learning for Diagnosis of Alzheimer's Disease with FDG-PET Neuroimaging,
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Investigating One-Class Classifiers to Diagnose Alzheimer's Disease from Handwriting,
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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.W.[Wen-Wu], 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

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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

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Handwriting-based Classifier Combination for Cognitive Impairment Prediction,
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ICIP20(325-329)
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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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ICIP19(789-793)
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Alzheimer's disease detection, MR images, multiscale features, multiscale CNN, feature fusion and enhancement BibRef

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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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Alzheimer's Disease Diagnosis with FDG-PET Brain Images By Using Multi-Level Features,
ICIP18(366-370)
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Feature extraction, Dementia, Magnetic resonance imaging, Positron emission tomography, Symmetric matrices, Alzheimer's Disease BibRef

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IPTA17(1-6)
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biomedical MRI, brain, diseases, image classification, learning (artificial intelligence), medical image processing, visual saliency BibRef

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Brain Tissue Classification of Alzheimer Disease Using Partial Volume Possibilistic Modeling: Application to ADNI Phantom Images,
IPTA17(1-5)
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biological tissues, biomedical MRI, brain, diseases, fuzzy set theory, image classification, image denoising, Possibilistic c-means algorithm BibRef

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DICTA17(1-6)
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biomedical MRI, diseases, face recognition, feature extraction, image classification, medical image processing, Training
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ICCV19(10570-10578)
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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

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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)
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backpropagation, biomedical MRI, diseases, feature extraction, image classification, medical image processing, neural nets, mild cognitive impairment BibRef

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Earlier:
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See also Network Edge Entropy from Maxwell-Boltzmann Statistics. BibRef

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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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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
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.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
BibRef

Zheng, X.[Xiao], Shi, J.[Jun], Ying, S.H.[Shi-Hui], 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
BibRef

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],
Identifying High Order Brain Connectome Biomarkers via Learning on Hypergraph,
MLMI16(1-9).
Springer DOI 1611
BibRef

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.R.[Xiao-Ran], Prasad, G.[Gautam], Lerman, K.[Kristina], Galstyan, A.[Aram], Thompson, P.M.[Paul M.],
Information-Theoretic Clustering of Neuroimaging Metrics Related to Cognitive Decline in the Elderly,
MCV15(13-23).
Springer DOI 1608
BibRef

Ben Ahmed, O.[Olfa], Benois-Pineau, J.[Jenny], Ben Amar, C.[Chokri], Aliara, M.[Michele], Catheline, G.[Gwenaelle],
Features-based approach for Alzheimer's disease diagnosis using visual pattern of water diffusion in tensor diffusion imaging,
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
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],
Joint Learning of Multiple Longitudinal Prediction Models by Exploring Internal Relations,
MLMI15(330-337).
Springer DOI 1511
BibRef

Guerrero, R., Ledig, C., Schmidt-Richberg, A., Rueckert, D.,
Group-Constrained Laplacian Eigenmaps: Longitudinal AD Biomarker Learning,
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],
An Hippocampal Segmentation Tool Within an Open Cloud Infrastructure,
ISCA15(193-200).
Springer DOI 1511
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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],
Identifying Abnormal Network Alterations Common to Traumatic Brain Injury and Alzheimer's Disease Patients Using Functional Connectome Data,
MLMI15(229-237).
Springer DOI 1511
BibRef

Huang, L.[Lei], Gao, Y.Z.[Yao-Zong], Jin, Y.[Yan], Thung, K.H.[Kim-Han], Shen, D.G.[Ding-Gang],
Soft-Split Sparse Regression Based Random Forest for Predicting Future Clinical Scores of Alzheimer's Disease,
MLMI15(246-254).
Springer DOI 1511
BibRef

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).
Springer DOI 1506
BibRef

Zhao, Y.[Yilu], He, L.H.[Liang-Hua],
Deep Learning in the EEG Diagnosis of Alzheimer's Disease,
DeepLearnV14(340-353).
Springer DOI 1504
BibRef

Aidos, H.[Helena], Duarte, J.[Joao], Fred, A.[Ana],
Identifying regions of interest for discriminating Alzheimer's disease from mild cognitive impairment,
ICIP14(21-25)
IEEE DOI 1502
Accuracy BibRef

Guerrero, R.[Ricardo], Ledig, C.[Christian], Rueckert, D.[Daniel],
Manifold Alignment and Transfer Learning for Classification of Alzheimer's Disease,
MLMI14(77-84).
Springer DOI 1410
BibRef

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],
Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer's Disease Diagnosis,
CVPR14(3089-3096)
IEEE DOI 1409
BibRef

Yan, Z.N.[Zhen-Nan], Zhang, S.T.[Shao-Ting], Liu, X.F.[Xiao-Feng], Metaxas, D.N.[Dimitris N.], Montillo, A.[Albert],
Accurate Whole-Brain Segmentation for Alzheimer's Disease Combining an Adaptive Statistical Atlas and Multi-atlas,
MCV13(65-73).
Springer DOI 1405
BibRef

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)
IEEE DOI 1402
Alzheimer's disease BibRef

Liu, S.[Sidong], Cai, W.D.[Wei-Dong], Wen, L.F.[Ling-Feng], Feng, D.D.[David Dagan],
Multiscale and multiorientation feature extraction with degenerative patterns for 3D neuroimaging retrieval,
ICIP12(1249-1252).
IEEE DOI 1302
BibRef

Sun, Z.[Zhuo], Jasinschi, R.S.[Radu S.], Veerman, J.A.C.[Jan A.C.],
A new method for data-driven multi-brain atlas generation,
ICIP14(3503-3507)
IEEE DOI 1502
Alzheimer's disease BibRef
Earlier: A1, A3, A2:
A method for detecting interstructural atrophy correlation in MRI brain images,
ICIP12(1253-1256).
IEEE DOI 1302
BibRef

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
BibRef

Bicacro, E.[Eduardo], Silveira, M.[Margarida], Marques, J.S.[Jorge S.],
Alternative feature extraction methods in 3D brain image-based diagnosis of Alzheimer's Disease,
ICIP12(1237-1240).
IEEE DOI 1302
BibRef

Mizotin, M.[Maxim], Benois-Pineau, J.[Jenny], Allard, M.[Michele], Catheline, G.[Gwenaelle],
Feature-based brain MRI retrieval for Alzheimer disease diagnosis,
ICIP12(1241-1244).
IEEE DOI 1302
BibRef

Dyrba, M.[Martin], Ewers, M.[Michael], Wegrzyn, M.[Martin], Kilimann, I.[Ingo], Plant, C.[Claudia], Oswald, A.[Annahita], Meindl, T.[Thomas], Pievani, M.[Michela], Bokde, A.L.W.[Arun L. W.], Fellgiebel, A.[Andreas], Filippi, M.[Massimo], Hampel, H.[Harald], Kloppel, S.[Stefan], Hauenstein, K.[Karlheinz], Kirste, T.[Thomas], Teipel, S.J.[Stefan J.],
Combining DTI and MRI for the Automated Detection of Alzheimer's Disease Using a Large European Multicenter Dataset,
MBIA12(18-28).
Springer DOI 1210
BibRef

Rueda, A.[Andrea], Arevalo, J.[John], Cruz, A.[Angel], Romero, E.[Eduardo], González, F.A.[Fabio A.],
Bag of Features for Automatic Classification of Alzheimer's Disease in Magnetic Resonance Images,
CIARP12(559-566).
Springer DOI 1209
BibRef

Hajiesmaeili, M.[Maryam], Bagherinakhjavanlo, B.[Bashir], Dehmeshki, J.[Jamshid], Ellis, T.[Tim],
Segmentation of the Hippocampus for Detection of Alzheimer's Disease,
ISVC12(I: 42-50).
Springer DOI 1209
BibRef

Wan, J.[Jing], Zhang, Z.L.[Zhi-Lin], Yan, J.W.[Jing-Wen], Li, T.[Taiyong], Rao, B.D.[Bhaskar D.], Fang, S.F.[Shiao-Fen], Kim, S.[Sungeun], Risacher, S.L.[Shannon L.], Saykin, A.J.[Andrew J.], Shen, L.[Li],
Sparse Bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer's disease,
CVPR12(940-947).
IEEE DOI 1208
BibRef

Wang, H.[Hua], Nie, F.P.[Fei-Ping], Huang, H.[Heng], Risacher, S.[Shannon], Ding, C.[Chris], Saykin, A.J.[Andrew J.], Shen, L.[Li],
Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance,
ICCV11(557-562).
IEEE DOI 1201
BibRef

Veerman, J.A.C., Soldea, O., Sahindrakar, P., Wan, Y., Jasinschi, R.S.,
Application of computational anatomy methods to MRI data for the diagnosis of Alzheimer'S disease,
ICIP11(1593-1596).
IEEE DOI 1201
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Filipovych, R.[Roman], Resnick, S.M.[Susan M.], Davatzikos, C.[Christos],
JointMMCC: Joint Maximum-Margin Classification and Clustering of Imaging Data,
MedImg(31), No. 5, May 2012, pp. 1124-1140.
IEEE DOI 1202
BibRef
Earlier:
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
BibRef

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
BibRef

Silveira, M.[Margarida], Marques, J.S.[Jorge S.],
Boosting Alzheimer Disease Diagnosis Using PET Images,
ICPR10(2556-2559).
IEEE DOI 1008
BibRef

Soldea, O.[Octavian], Ekin, A.[Ahmet], Soldea, D.F.[Diana F.], Unay, D.[Devrim], Cetin, M.[Mujdat], Ercil, A.[Aytul], Uzunbas, M.G.[Mustafa Gokhan], Firat, Z.[Zeynep], Cihangiroglu, M.[Mutlu], Initiative, T.A.D.N.[The Alzheimers Disease Neuroimaging],
Segmentation of Anatomical Structures in Brain MR Images Using Atlases in FSL: A Quantitative Approach,
ICPR10(2592-2595).
IEEE DOI 1008
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Wang, J.N.[Jing-Nan], Ekin, A.[Ahmet], de Haan, G.[Gerard],
Shape analysis of brain ventricles for improved classification of Alzheimer's patients,
ICIP08(2252-2255).
IEEE DOI 0810
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dos Santos, W.P.[Wellington P.], de Souza, R.E.[Ricardo E.], Silva, A.F.D.[Ascendino F. D.], Santos Filho, P.B.[Plínio B.],
Evaluation of Alzheimer's Disease by Analysis of MR Images Using Multilayer Perceptrons, Polynomial Nets and Kohonen LVQ Classifiers,
MIRAGE07(12-22).
Springer DOI 0703
BibRef

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).
IEEE DOI 0609
BibRef

Teverovskiy, L., Liu, Y.,
Truly 3D Midsagittal Plane Extraction for Robust Neuroimage Registration,
CMU-RI-TR-04-21, March, 2004.
HTML Version. 0501
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Brain, Parkinson's Disease .