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.,
Functional brain image classification using association rules defined
over discriminant regions,
PRL(33), No. 12, 1 September 2012, pp. 1666-1672.
Elsevier DOI
1208
Functional brain imaging; Alzheimer's Disease; Fisher
Discriminant
Ratio; Association rules
for the Alzheimer's Disease Neuroimaging Initiative,
BibRef
Mesrob, L.,
Magnin, B.,
Colliot, O.,
Sarazin, M.,
Hahn-Barma, V.,
Dubois, B.,
Gallinari, P.,
Lehericy, S.,
Kinkingnehun, S.,
Benali, H.,
Identification of atrophy patterns in Alzheimer's disease based on SVM
feature selection and anatomical parcellation,
BMVA(2009), No. 7, 2009, pp. 1-9.
PDF File.
1209
BibRef
Janousova, E.[Eva],
Vounou, M.[Maria],
Wolz, R.[Robin],
Gray, K.R.[Katherine R.],
Rueckert, D.[Daniel],
Montana, G.[Giovanni],
Biomarker discovery for sparse classification of brain images in
Alzheimer's disease,
BMVA(2012), No. 2, 2012, pp. 1-11.
PDF File.
1209
BibRef
Cuingnet, R.[Rémi],
Glaunès, J.A.[Joan Alexis],
Chupin, M.[Marie],
Benali, H.[Habib],
Colliot, O.[Olivier],
Spatial and Anatomical Regularization of SVM: A General Framework for
Neuroimaging Data,
PAMI(35), No. 3, March 2013, pp. 682-696.
IEEE DOI
1303
BibRef
Earlier:
Anatomical Regularization on Statistical Manifolds for the
Classification of Patients with Alzheimer's Disease,
MLMI11(201-208).
Springer DOI
1109
BibRef
Zhao, M.B.[Ming-Bo],
Chan, R.H.M.,
Tang, P.[Peng],
Chow, T.W.S.,
Wong, S.W.H.,
Trace Ratio Linear Discriminant Analysis for Medical Diagnosis:
A Case Study of Dementia,
SPLetters(20), No. 5, May 2013, pp. 431-434.
IEEE DOI
1304
BibRef
Zhao, M.B.[Ming-Bo],
Chan, R.H.M.,
Chow, T.W.S.,
Tang, P.,
Compact Graph based Semi-Supervised Learning for Medical Diagnosis in
Alzheimer's Disease,
SPLetters(21), No. 10, October 2014, pp. 1192-1196.
IEEE DOI
1407
Classification algorithms
BibRef
Huang, S.[Shuai],
Li, J.[Jing],
Ye, J.P.[Jie-Ping],
Fleisher, A.[Adam],
Chen, K.W.[Ke-Wei],
Wu, T.[Teresa],
Reiman, E.[Eric],
A Sparse Structure Learning Algorithm for Gaussian Bayesian Network
Identification from High-Dimensional Data,
PAMI(35), No. 6, June 2013, pp. 1328-1342.
IEEE DOI
1305
the Alzheimer's Disease Neuroimaging Initiative.
Apply to brain connectivity modeling.
BibRef
Komlagan, M.[Mawulawoé],
Ta, V.T.[Vinh-Thong],
Pan, X.Y.[Xing-Yu],
Domenger, J.P.[Jean-Philippe],
Coupé, D.L.C.P.[D. Louis Collins Pierrick],
Anatomically Constrained Weak Classifier Fusion for Early Detection of
Alzheimer's Disease,
MLMI14(141-148).
Springer DOI
1410
the Alzheimer's Disease Neuroimaging Initiative
BibRef
Kodewitz, A.[Andreas],
Lelandais, S.[Sylvie],
Montagne, C.[Christophe],
Vigneron, V.[Vincent],
Alzheimer's disease early detection from sparse data using brain
importance maps,
ELCVIA(12), No. 1, 2013, pp. xx-yy.
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:
The Alzheimer's Disease Neuroimaging Initiative,
The clique potential of Markov random field in a random experiment
for estimation of noise levels in 2D brain MRI,
IJIST(23), No. 4, 2013, pp. 304-313.
DOI Link
1312
magnetic resonance imaging
BibRef
Osadebey, M.[Michael],
Bouguila, N.[Nizar],
Arnold, D.[Douglas], And:
The Alzheimer's Disease Neuroimaging Initiative.
Four-neighborhood clique kernel: A general framework for Bayesian and
variational techniques of noise reduction in magnetic resonance
images of the brain,
IJIST(24), No. 3, 2014, pp. 224-238.
DOI Link
1408
magnetic resonance imaging
BibRef
Zhao, X.J.[Xiao-Jie],
Wen, X.T.[Xiao-Tong],
Shen, J.H.[Jia-Hui],
Hong, H.[Hao],
Yao, L.[Li],
An improved fast marching method and its application in Alzheimer's
disease,
IJIST(23), No. 4, 2013, pp. 346-352.
DOI Link
1312
fast marching method
BibRef
Rueda, A.,
Gonzalez, F.A.,
Romero, E.,
Extracting Salient Brain Patterns for Imaging-Based Classification of
Neurodegenerative Diseases,
MedImg(33), No. 6, June 2014, pp. 1262-1274.
IEEE DOI
1407
Brain modeling
BibRef
Wan, J.,
Zhang, Z.,
Rao, B.D.,
Fang, S.,
Yan, J.,
Saykin, A.J.,
Shen, L.,
Identifying the Neuroanatomical Basis of Cognitive Impairment in
Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse
Bayesian Learning,
MedImg(33), No. 7, July 2014, pp. 1475-1487.
IEEE DOI
1407
Alzheimer's disease
BibRef
Yang, W.[Wenji],
Huang, W.[Wei],
Chen, S.X.[Shan-Xue],
Partial Volume Correction on ASL-MRI and Its Application on Alzheimer's
Disease Diagnosis,
IEICE(E97-D), No. 11, November 2014, pp. 2912-2918.
WWW Link.
1412
BibRef
Poynton, C.B.,
Jenkinson, M.,
Adalsteinsson, E.,
Sullivan, E.V.,
Pfefferbaum, A.,
Wells, W.,
Quantitative Susceptibility Mapping by Inversion of a Perturbation
Field Model: Correlation With Brain Iron in Normal Aging,
MedImg(34), No. 1, January 2015, pp. 339-353.
IEEE DOI
1502
Fourier analysis
BibRef
Liu, X.W.[Xin-Wang],
Zhou, L.P.[Lu-Ping],
Wang, L.[Lei],
Zhang, J.[Jian],
Yin, J.P.[Jian-Ping],
Shen, D.G.[Ding-Gang],
An efficient radius-incorporated MKL algorithm for Alzheimer's
disease prediction,
PR(48), No. 7, 2015, pp. 2141-2150.
Elsevier DOI
1504
Multiple kernel learning
BibRef
Aggarwal, N.[Namita],
Rana, B.[Bharti],
Agrawal, R.K.,
3d discrete wavelet transform for computer aided diagnosis of
Alzheimer's disease using t1-weighted brain MRI,
IJIST(25), No. 2, 2015, pp. 179-190.
DOI Link
1506
BibRef
Earlier:
Computer Aided Diagnosis of Alzheimer's Disease from MRI Brain Images,
ICIAR12(II: 259-267).
Springer DOI
1206
Alzheimer's disease
BibRef
Li, Y.,
Pan, J.,
Long, J.,
Yu, T.,
Wang, F.,
Yu, Z.,
Wu, W.,
Multimodal BCIs: Target Detection, Multidimensional Control, and
Awareness Evaluation in Patients With Disorder of Consciousness,
PIEEE(104), No. 2, February 2016, pp. 332-352.
IEEE DOI
1601
Biomedical signal processing
BibRef
Warren, D.J.,
Kellis, S.,
Nieveen, J.G.,
Wendelken, S.M.,
Dantas, H.,
Davis, T.S.,
Hutchinson, D.T.,
Normann, R.A.,
Clark, G.A.,
Mathews, V.J.,
Recording and Decoding for Neural Prostheses,
PIEEE(104), No. 2, February 2016, pp. 374-391.
IEEE DOI
1601
Biomedical signal processing
BibRef
Seo, D.H.[Do-Hyung],
Ho, J.[Jeffrey],
Vemuri, B.C.[Baba C.],
Covariant Image Representation with Applications to Classification
Problems in Medical Imaging,
IJCV(116), No. 2, January 2016, pp. 190-209.
Springer DOI
1602
Apply to MR for Alzheimers and MR detection of seizures.
BibRef
Liu, M.H.[Man-Hua],
Zhang, D.Q.[Dao-Qiang],
Shen, D.G.[Ding-Gang],
Relationship Induced Multi-Template Learning for Diagnosis of
Alzheimer's Disease and Mild Cognitive Impairment,
MedImg(35), No. 6, June 2016, pp. 1463-1474.
IEEE DOI
1606
BibRef
Earlier:
Inherent Structure-Guided Multi-view Learning for Alzheimer's Disease
and Mild Cognitive Impairment Classification,
MLMI15(296-303).
Springer DOI
1511
Alzheimer's disease
BibRef
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.[Xiaoli],
Yang, J.[Jinzhu],
Zhao, D.[Dazhe],
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
Peng, J.[Jialin],
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],
Neuroimaging computer-aided diagnosis systems for Alzheimer's disease,
IJIST(29), No. 1, March 2019, pp. 83-94.
WWW Link.
1902
BibRef
He, M.,
Baker, S.L.,
Shah, V.D.,
Lockhart, S.N.,
Jagust, W.J.,
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],
Handwriting analysis to support neurodegenerative diseases diagnosis:
A review,
PRL(121), 2019, pp. 37-45.
Elsevier DOI
1904
BibRef
Li, Y.,
Yang, H.,
Lei, B.,
Liu, J.,
Wee, C.,
Novel Effective Connectivity Inference Using Ultra-Group Constrained
Orthogonal Forward Regression and Elastic Multilayer Perceptron
Classifier for MCI Identification,
MedImg(38), No. 5, May 2019, pp. 1227-1239.
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],
An improved machine learning technique based on downsized KPCA for
Alzheimer's disease classification,
IJIST(29), No. 2, June 2019, pp. 121-131.
WWW Link.
1906
BibRef
Chen, J.,
Liu, J.,
Calhoun, V.D.,
Translational Potential of Neuroimaging Genomic Analyses to Diagnosis
and Treatment in Mental Disorders,
PIEEE(107), No. 5, May 2019, pp. 912-927.
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
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).
Springer DOI
1807
BibRef
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
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
BibRef
Zhang, J.[Jun],
Liu, M.[Mingxia],
An, L.[Le],
Gao, Y.[Yaozong],
Shen, D.G.[Ding-Gang],
Landmark-Based Alzheimer's Disease Diagnosis Using Longitudinal
Structural MR Images,
MCV16(35-45).
Springer DOI
1711
BibRef
Endo, T.,
Ukita, N.,
Tanaka, H.,
Hagita, N.,
Nakamura, S.,
Adachi, H.,
Ikeda, M.,
Kazui, H.,
Kudo, T.,
Initial response time measurement in eye movement for dementia
screening test,
MVA17(262-265)
DOI Link
1708
Cameras, Dementia, Facial features, Feature extraction, Iris, Time, factors
BibRef
Ding, J.[Junwei],
Huang, Q.[Qiu],
Prediction of MCI to AD conversion using Laplace Eigenmaps learned
from FDG and MRI images of AD patients and healthy controls,
ICIVC17(660-664)
IEEE DOI
1708
Diseases, Hippocampus, Image reconstruction,
Magnetic resonance imaging, Prognostics and health management,
Proteins, Support vector machines, alzheimers, image processing,
manifold, medical image, prediction
BibRef
Shukla, P.,
Gupta, T.,
Saini, A.,
Singh, P.,
Balasubramanian, R.,
A Deep Learning Frame-Work for Recognizing Developmental Disorders,
WACV17(705-714)
IEEE DOI
1609
Atmospheric modeling, Autism, Computer vision, Face, Genetics,
Neural networks, Support, vector, machines
BibRef
Wang, J.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
See also Network Edge Entropy from Maxwell-Boltzmann Statistics.
BibRef
Bernardes, R.[Rui],
Silva, G.[Gilberto],
Chiquita, S.[Samuel],
Serranho, P.[Pedro],
Ambrósio, A.F.[António Francisco],
Retinal Biomarkers of Alzheimer's Disease: Insights from Transgenic
Mouse Models,
ICIAR17(541-550).
Springer DOI
1706
BibRef
Kumar, K.,
Desrosiers, C.,
Chaddad, A.,
Toews, M.,
Spatially constrained sparse regression for the data-driven discovery
of Neuroimaging biomarkers,
ICPR16(2162-2167)
IEEE DOI
1705
Alzheimer's disease, Biomarkers, Brain modeling, Data models,
Databases, Neuroimaging
BibRef
Montenegro, J.M.F.[Juan Manuel Fernandez],
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
BibRef
Montenegro, J.M.F.[Juan Manuel Fernandez],
Gkelias, A.[Athanasios],
Argyriou, V.[Vasileios],
Emotion Understanding Using Multimodal Information Based on
Autobiographical Memories for Alzheimer's Patients,
Assist16(I: 252-268).
Springer DOI
1704
BibRef
Cury, C.[Claire],
Lorenzi, M.[Marco],
Cash, D.[David],
Nicholas, J.M.[Jennifer M.],
Routier, A.[Alexandre],
Rohrer, J.[Jonathan],
Ourselin, S.[Sebastien],
Durrleman, S.[Stanley],
Modat, M.[Marc],
Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of
Early Changes in Neurodegenerative Disease,
SeSAME16(63-75).
Springer DOI
1703
BibRef
Rudas, J.[Jorge],
Martínez, D.[Darwin],
Demertzi, A.[Athena],
Di Perri, C.[Carol],
Heine, L.[Lizette],
Tshibanda, L.[Luaba],
Soddu, A.[Andrea],
Multivariate Functional Network Connectivity for Disorders of
Consciousness,
CIARP16(434-442).
Springer DOI
1703
BibRef
Shakeri, M.[Mahsa],
Lombaert, H.[Herve],
Tripathi, S.[Shashank],
Kadoury, S.[Samuel],
Deep Spectral-Based Shape Features for Alzheimer's Disease
Classification,
SeSAME16(15-24).
Springer DOI
1703
BibRef
Bhatkoti, P.,
Paul, M.,
Early diagnosis of Alzheimer's disease: A multi-class deep learning
framework with modified k-sparse autoencoder classification,
ICVNZ16(1-5)
IEEE DOI
1701
Alzheimer's disease
BibRef
Joshi, S.H.[Shantanu H.],
Xie, Q.[Qian],
Kurtek, S.[Sebastian],
Srivastava, A.[Anuj],
Laga, H.[Hamid],
Surface Shape Morphometry for Hippocampal Modeling in Alzheimer's
Disease,
DICTA16(1-8)
IEEE DOI
1701
Diseases
BibRef
Aderghal, K.[Karim],
Boissenin, M.[Manuel],
Benois-Pineau, J.[Jenny],
Catheline, G.[Gwenaëlle],
Afdel, K.[Karim],
Classification of sMRI for AD Diagnosis with Convolutional Neuronal
Networks: A Pilot 2-D+ epsilon Study on ADNI,
MMMod17(I: 690-701).
Springer DOI
1701
BibRef
Tang, X.,
Albert, M.,
Miller, M.I.[Michael I.],
Younes, L.[Laurent],
Change Point Estimation of the Hippocampal Volumes in Alzheimer's
Disease,
CRV16(358-361)
IEEE DOI
1612
Alzheimer's disease
BibRef
Kim, W.H.[Won Hwa],
Kim, H.J.[Hyunwoo J.],
Adluru, N.[Nagesh],
Singh, V.[Vikas],
Latent Variable Graphical Model Selection Using Harmonic Analysis:
Applications to the Human Connectome Project (HCP),
CVPR16(2443-2451)
IEEE DOI
1612
BibRef
Yang, X.[Xi],
Jin, Y.[Yan],
Chen, X.B.[Xiao-Bo],
Zhang, H.[Han],
Li, G.[Gang],
Shen, D.G.[Ding-Gang],
Functional Connectivity Network Fusion with Dynamic Thresholding for
MCI Diagnosis,
MLMI16(246-253).
Springer DOI
1611
BibRef
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
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.[Xiaoran],
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
BibRef
Amoroso, N.[Nicola],
Tangaro, S.[Sabina],
Errico, R.[Rosangela],
Garuccio, E.[Elena],
Monda, A.[Anna],
Sensi, F.[Francesco],
Tateo, A.[Andrea],
Bellotti, R.[Roberto], for the Alzheimer's Disease Neuroimaging Initiative,
An Hippocampal Segmentation Tool Within an Open Cloud Infrastructure,
ISCA15(193-200).
Springer DOI
1511
BibRef
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], Alzheimers Disease Neuroimaging Initiative,
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
Stühler, E.[Elisabeth],
Berthold, M.R.[Michael R.],
Dementia-Related Features in Longitudinal MRI:
Tracking Keypoints over Time,
MCV14(59-70).
Springer DOI
1501
BibRef
Díaz, G.[Gloria],
García-Polo, P.[Pablo],
Mato, V.[Virginia],
Alfayate, E.[Eva],
Hernández-Tamames, J.A.[Juan Antonio],
Malpica, N.[Norberto],
Predicting Very Early Stage Mild Cognitive Impairment Based on a
Voxel-wise Arterial Spin Labeling Analysis,
CIARP14(714-721).
Springer DOI
1411
BibRef
Li, F.[Feng],
Tran, L.[Loc],
Thung, K.H.[Kim-Han],
Ji, S.W.[Shui-Wang],
Shen, D.G.[Ding-Gang],
Li, J.[Jiang],
Robust Deep Learning for Improved Classification of AD/MCI Patients,
MLMI14(240-247).
Springer DOI
1410
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
Hu, C.H.[Chen-Hui],
Hua, X.[Xue],
Thompson, P.M.[Paul M.],
El Fakhri, G.[Georges],
Li, Q.Z.[Quan-Zheng],
Inferring Sources of Dementia Progression with Network Diffusion Model,
MLMI14(42-49).
Springer DOI
1410
BibRef
Bron, E.[Esther],
Smits, M.[Marion],
van Swieten, J.[John],
Niessen, W.[Wiro],
Klein, S.[Stefan],
Feature Selection Based on SVM Significance Maps for Classification of
Dementia,
MLMI14(272-279).
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
Simoes, R.[Rita],
Slump, C.[Cornelis],
van Cappellen-van Walsum, A.M.[Anne-Marie],
Using local texture maps of brain MR images to detect Mild Cognitive
Impairment,
ICPR12(153-156).
WWW Link.
1302
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
Nir, T.M.[Talia M.],
Jahanshad, N.[Neda],
Toga, A.W.[Arthur W.],
Jack, C.R.[Clifford R.],
Weiner, M.W.[Michael W.],
Thompson, P.M.[Paul M.],
Connectivity Network Breakdown Predicts Imminent Volumetric Atrophy in
Early Mild Cognitive Impairment,
MBIA12(41-50).
Springer DOI
1210
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
BibRef
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
Gray, K.R.[Katherine R.],
Aljabar, P.[Paul],
Heckemann, R.A.[Rolf A.],
Hammers, A.[Alexander],
Rueckert, D.[Daniel],
Random Forest-Based Manifold Learning for Classification of Imaging
Data in Dementia,
MLMI11(159-166).
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
Tosun, D.[Duygu],
Weiner, M.W.[Michael W.],
Schuff, N.[Norbert],
Rosen, H.[Howard],
Miller, B.L.[Bruce L.],
Joint Independent Component Analysis of Brain Perfusion and Structural
Magnetic Resonance Images in Dementia,
ICPR10(2720-2723).
IEEE DOI
1008
BibRef
Kondou, Y.[Youhei],
Kawasumi, M.[Mikiko],
Yamamoto, O.[Osami],
Yamada, M.[Muneo],
Yamamoto, S.[Shin],
Nakanno, T.[Tomoaki],
Study of Early Screening Method of Dementia and Its Systemization,
MVA09(82-).
PDF File.
0905
BibRef
Akgul, C.B.[Ceyhun Burak],
Ekin, A.[Ahmet],
A Probabilistic Information Fusion Approach to MR-based Automated
Diagnosis of Dementia,
ICPR10(265-268).
IEEE DOI
1008
BibRef
Akgül, C.B.[Ceyhun Burak],
Ünay, D.[Devrim],
Ekin, A.[Ahmet],
Automated diagnosis of Alzheimer's disease using image similarity and
user feedback,
CIVR09(Article No 34).
DOI Link
0907
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
BibRef
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
BibRef
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
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain, Parkinson's Disease .