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Functional brain imaging; Alzheimer's Disease; Fisher
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Springer DOI
1109
Alzheimers
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Brain modeling
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Alzheimer's disease
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Alzheimer's disease
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Biomedical signal processing
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Earlier:
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Alzheimer's disease
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Zhu, W.Y.[Wen-Yong],
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2109
Feature extraction, Diseases, Pathology, Deep learning,
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1804
biomedical MRI, brain, cognition, diseases, graph theory,
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Springer DOI
1511
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Springer DOI
1211
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Suk, H.I.[Heung-Il],
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Springer DOI
1611
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Shen, D.G.[Ding-Gang],
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MLMI15(255-262).
Springer DOI
1511
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Earlier: A1, A2, A6, Only:
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Springer DOI
1410
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Shen, D.G.[Ding-Gang],
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Springer DOI
1611
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Springer DOI
1611
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MultiCost: Multi-stage Cost-sensitive Classification of Alzheimer's
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1109
BibRef
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Zhang, D.Q.[Dao-Qiang],
Wee, C.Y.[Chong-Yaw],
Shen, D.G.[Ding-Gang],
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Springer DOI
1210
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Li, Y.[Yang],
Yap, P.T.[Pew-Thian],
Shen, D.G.[Ding-Gang],
Adni,
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IEEE DOI
1106
T1-weighted MRI for mild cognitive impairment.
BibRef
Li, Z.,
Suk, H.I.,
Shen, D.,
Li, L.,
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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
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PAMI(38), No. 11, November 2016, pp. 2269-2283.
IEEE DOI
1610
BibRef
Earlier:
Discriminative Brain Effective Connectivity Analysis for Alzheimer's
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CVPR13(2243-2250)
IEEE DOI
1309
Bayes methods.
Alzheimer's Disease
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Wang, L.[Lei],
Ogunbona, P.O.[Philip O.],
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CVPR14(3097-3104)
IEEE DOI
1409
Graphical LASSO
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PR(63), No. 1, 2017, pp. 171-181.
Elsevier DOI
1612
BibRef
Earlier:
Nonlinear Graph Fusion for Multi-modal Classification of Alzheimer's
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MLMI15(77-84).
Springer DOI
1511
Multiple modalities
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Shi, B.[Bibo],
Chen, Y.[Yani],
Zhang, P.[Pin],
Smith, C.D.[Charles D.],
Liu, J.D.[Jun-Dong],
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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
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Hobbs, K.[Kevin],
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DOI Link
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Guerrero, R.,
Ledig, C.,
Schmidt-Richberg, A.,
Rueckert, D.,
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PR(63), No. 1, 2017, pp. 570-582.
Elsevier DOI
1612
Alzheimer's disease
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Zhang, J.,
Gao, Y.,
Gao, Y.,
Munsell, B.C.,
Shen, D.,
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IEEE DOI
1612
Feature extraction
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Nanni, L.[Loris],
Salvatore, C.[Christian],
Cerasa, A.[Antonio],
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Alzheimer's Disease
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Lei, B.,
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Chen, S.,
Ni, D.,
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1704
Cybernetics
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Alam, S.[Saruar],
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Initiative, T.A.D.N.[The Alzheimer's Disease Neuroimaging],
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FreeSurfer, CIVET, KPCA, PCA, LDA, MK-SVM
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Dadar, M.,
Pascoal, T.A.,
Manitsirikul, S.,
Misquitta, K.,
Fonov, V.S.,
Tartaglia, M.C.,
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Rosa-Neto, P.,
Carmichael, O.T.,
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1708
Alzheimer's disease, Image segmentation, Lesions,
Magnetic resonance imaging, Robustness, Alzheimer's disease,
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Alzheimer's, disease
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Cao, P.[Peng],
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Elsevier DOI
1804
Alzheimer's disease, Regression, Sparse learning,
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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.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],
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
Cilia, N.D.[Nicole Dalia],
de Stefano, C.[Claudio],
Fontanella, F.[Francesco],
Molinara, M.[Mario],
di Freca, A.S.[Alessandra Scotto],
Handwriting Analysis to Support Alzheimer's Disease Diagnosis: A
Preliminary Study,
CAIP19(II:143-151).
Springer DOI
1909
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
Khagi, B.[Bijen],
Kwon, G.R.[Goo-Rak],
Lama, R.[Ramesh],
Comparative analysis of Alzheimer's disease classification by CDR level
using CNN, feature selection, and machine-learning techniques,
IJIST(29), No. 3, September 2019, pp. 297-310.
DOI Link
1908
BibRef
Im, J.J.[Jooyeon J.],
Jeong, H.[Hyeonseok],
Oh, J.K.[Jin Kyoung],
Chung, Y.A.[Yong-An],
Song, I.U.[In-Uk],
Lee, K.S.[Kwang-Soo],
Effects of nicergoline treatment on regional cerebral blood flow in
early Alzheimer's disease,
IJIST(29), No. 3, September 2019, pp. 323-328.
DOI Link
1908
BibRef
Ahmad, F.[Fayyaz],
Zulifqar, H.[Hifza],
Malik, T.[Tamoor],
Classification of Alzheimer disease among susceptible brain regions,
IJIST(29), No. 3, September 2019, pp. 222-233.
DOI Link
1908
BibRef
Latha, M.[Manohar],
Kavitha, G.[Ganesan],
Assessment of severity in neuropsychiatric disorders based on radiomic
features with prior shape level set and metaheuristic algorithms,
IJIST(29), No. 3, September 2019, pp. 210-221.
DOI Link
1908
BibRef
Wang, J.J.[Jian-Jia],
Huo, J.Y.[Jia-Yu],
Zhang, L.C.[Li-Chi],
Thermodynamic edge entropy in Alzheimer's disease,
PRL(125), 2019, pp. 570-575.
Elsevier DOI
1909
Alzheimer's disease, Maxwell-Boltzmann statistics, Network edge entropy
BibRef
Zhou, T.,
Liu, M.,
Thung, K.,
Shen, D.,
Latent Representation Learning for Alzheimer's Disease Diagnosis With
Incomplete Multi-Modality Neuroimaging and Genetic Data,
MedImg(38), No. 10, October 2019, pp. 2411-2422.
IEEE DOI
1910
Magnetic resonance imaging, Feature extraction, Genetics,
Neuroimaging, Alzheimer's disease, Positron emission tomography,
latent representation space
BibRef
Huang, W.,
Luo, M.,
Liu, X.,
Zhang, P.,
Ding, H.,
Xue, W.,
Ni, D.,
Arterial Spin Labeling Images Synthesis From sMRI Using Unbalanced
Deep Discriminant Learning,
MedImg(38), No. 10, October 2019, pp. 2338-2351.
IEEE DOI
1910
Deep learning, Dementia, Biomedical imaging, Medical diagnosis,
Magnetic resonance imaging, Magnetic resonance imaging (MRI),
machine learning
BibRef
Li, J.[Jiaye],
Wu, L.[Lin],
Wen, G.Q.[Guo-Qiu],
Li, Z.[Zhi],
Exclusive feature selection and multi-view learning for Alzheimer's
Disease,
JVCIR(64), 2019, pp. 102605.
Elsevier DOI
1911
Alzheimer's Disease, Multi-view, Exclusive lasso learning,
Feature selection, Sparse learning
BibRef
Fang, X.S.[Xu-Sheng],
Liu, Z.B.[Zhen-Bing],
Xu, M.C.[Ming-Chang],
Ensemble of deep convolutional neural networks based multi-modality
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IET-IPR(14), No. 2, February 2020, pp. 318-326.
DOI Link
2001
BibRef
Bharanidharan, N.,
Rajaguru, H.[Harikumar],
Performance enhancement of swarm intelligence techniques in dementia
classification using dragonfly-based hybrid algorithms,
IJIST(30), No. 1, 2020, pp. 57-74.
DOI Link
2002
DA-PSO, dementia, hybrid swarm intelligence, MRI, OASIS
BibRef
Choi, J.Y.,
Lee, B.,
Combining of Multiple Deep Networks via Ensemble Generalization Loss,
Based on MRI Images, for Alzheimer's Disease Classification,
SPLetters(27), 2020, pp. 206-210.
IEEE DOI
2002
Alzheimer's disease classification, ensemble deep learning, generalization loss
BibRef
Kam, T.,
Zhang, H.,
Jiao, Z.,
Shen, D.,
Deep Learning of Static and Dynamic Brain Functional Networks for
Early MCI Detection,
MedImg(39), No. 2, February 2020, pp. 478-487.
IEEE DOI
2002
Ions, Noise measurement, Manganese, Diagnosis,
convolutional neural networks, brain network,
functional MRI
BibRef
Lei, B.Y.[Bai-Ying],
Yang, M.Y.[Meng-Ya],
Yang, P.[Peng],
Zhou, F.[Feng],
Hou, W.[Wen],
Zou, W.B.[Wen-Bin],
Li, X.[Xia],
Wang, T.F.[Tian-Fu],
Xiao, X.H.[Xiao-Hua],
Wang, S.Q.[Shu-Qiang],
Deep and joint learning of longitudinal data for Alzheimer's disease
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PR(102), 2020, pp. 107247.
Elsevier DOI
2003
Alzheimer's disease, Longitudinal scores prediction,
Joint learning, Correntropy, Deep polynomial network
BibRef
Lian, C.F.[Chun-Feng],
Liu, M.X.[Ming-Xia],
Zhang, J.[Jun],
Shen, D.G.[Ding-Gang],
Hierarchical Fully Convolutional Network for Joint Atrophy
Localization and Alzheimer's Disease Diagnosis Using Structural MRI,
PAMI(42), No. 4, April 2020, pp. 880-893.
IEEE DOI
2003
Feature extraction, Solid modeling, Atrophy, Brain modeling,
Alzheimer's disease, Medical diagnosis, Support vector machines,
structural MRI
BibRef
Zhang, J.[Jun],
Liu, M.X.[Ming-Xia],
An, L.[Le],
Gao, Y.Z.[Yao-Zong],
Shen, D.G.[Ding-Gang],
Landmark-Based Alzheimer's Disease Diagnosis Using Longitudinal
Structural MR Images,
MCV16(35-45).
Springer DOI
1711
BibRef
Zhao, Y.,
Zhao, Y.,
Durongbhan, P.,
Chen, L.,
Liu, J.,
Billings, S.A.,
Zis, P.,
Unwin, Z.C.,
de Marco, M.,
Venneri, A.,
Blackburn, D.J.,
Sarrigiannis, P.G.,
Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based
on EEG Recordings With the Application on the Diagnosis of
Alzheimer's Disease,
MedImg(39), No. 5, May 2020, pp. 1571-1581.
IEEE DOI
2005
Alzheimer's disease, dementia, visualisation,
system identification, machine learning
BibRef
Zhang, Y.[Yingteng],
Liu, S.[Shenquan],
Yu, X.L.[Xiao-Li],
Longitudinal structural MRI analysis and classification in
Alzheimer's disease and mild cognitive impairment,
IJIST(30), No. 2, 2020, pp. 421-433.
DOI Link
2005
Alzheimer's disease, gray matter volume, longitudinal analysis,
longitudinal classification, mild cognitive impairment
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Allioui, H.[Hanane],
Sadgal, M.[Mohamed],
Elfazziki, A.[Aziz],
Utilization of a convolutional method for Alzheimer disease diagnosis,
MVA(31), No. 4, April 2020, pp. Article25.
Springer DOI
2005
BibRef
Brand, L.,
Nichols, K.,
Wang, H.,
Shen, L.,
Huang, H.,
Joint Multi-Modal Longitudinal Regression and Classification for
Alzheimer's Disease Prediction,
MedImg(39), No. 6, June 2020, pp. 1845-1855.
IEEE DOI
2006
Alzheimer's disease, biomarker identification,
joint regression-classification, longitudinal, multi-modal, multi-task
BibRef
Koh, J.E.W.[Joel En Wei],
Jahmunah, V.[Vicnesh],
Pham, T.H.[The-Hanh],
Oh, S.L.[Shu Lih],
Ciaccio, E.J.[Edward J],
Acharya, U.R.[U Rajendra],
Yeong, C.H.[Chai Hong],
Fabell, M.K.M.[Mohd Kamil Mohd],
Rahmat, K.[Kartini],
Vijayananthan, A.[Anushya],
Ramli, N.[Norlisah],
Automated detection of Alzheimer's disease using bi-directional
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PRL(135), 2020, pp. 106-113.
Elsevier DOI
2006
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Lella, E.[Eufemia],
Vessio, G.[Gennaro],
Ensembling complex network 'perspectives' for mild cognitive
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Elsevier DOI
2008
Decision support systems, Mild cognitive impairment,
Diffusion-weighted imaging, Complex networks, Artificial neural networks
BibRef
Bharanidharan, N.,
Rajaguru, H.[Harikumar],
Improved chicken swarm optimization to classify dementia MRI images
using a novel controlled randomness optimization algorithm,
IJIST(30), No. 3, 2020, pp. 605-620.
DOI Link
2008
CSO, dementia, ICSO, meta-heuristic, MRI
BibRef
Pan, Y.,
Liu, M.,
Lian, C.,
Xia, Y.,
Shen, D.,
Spatially-Constrained Fisher Representation for Brain Disease
Identification With Incomplete Multi-Modal Neuroimages,
MedImg(39), No. 9, September 2020, pp. 2965-2975.
IEEE DOI
2009
Magnetic resonance imaging, Feature extraction, Diseases,
Positron emission tomography, Medical diagnosis, Brain modeling,
PET
BibRef
Bône, A.[Alexandre],
Colliot, O.[Olivier],
Durrleman, S.[Stanley],
Initiative, T.A.D.N.[The Alzheimer's Disease Neuroimaging],
Learning the spatiotemporal variability in longitudinal shape data sets,
IJCV(128), No. 12, December 2020, pp. 2873-2896.
Springer DOI
2010
BibRef
Earlier: A1, A2, A2:
Learning Distributions of Shape Trajectories from Longitudinal
Datasets: A Hierarchical Model on a Manifold of Diffeomorphisms,
CVPR18(9271-9280)
IEEE DOI
1812
Shape, Trajectory, Computational modeling, Manifolds,
Spatiotemporal phenomena, Data models, Numerical models
BibRef
Debavelaere, V.[Vianney],
Durrleman, S.[Stanley],
Allassonnière, S.[Stéphanie],
Initiative, T.A.D.N.[The Alzheimer's Disease Neuroimaging],
Learning the Clustering of Longitudinal Shape Data Sets into a Mixture
of Independent or Branching Trajectories,
IJCV(128), No. 12, December 2020, pp. 2794-2809.
Springer DOI
2010
BibRef
Chevallier, J.[Juliette],
Debavelaere, V.[Vianney],
Allassonnière, S.[Stéphanie],
A Coherent Framework for Learning Spatiotemporal Piecewise-Geodesic
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SIIMS(14), No. 1, 2021, pp. 349-388.
DOI Link
2104
BibRef
Du, L.,
Liu, F.,
Liu, K.,
Yao, X.,
Risacher, S.L.,
Han, J.,
Saykin, A.J.,
Shen, L.,
Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk
Factors via a Dirty Multi-Task Learning Method,
MedImg(39), No. 11, November 2020, pp. 3416-3428.
IEEE DOI
2011
Imaging, Genetics, Task analysis, Brain, Neuroimaging, Dementia,
Brain imaging genetics, sparse canonical correlation analysis,
the dirty multi-task SCCA
BibRef
Saied, I.,
Arslan, T.,
Chandran, S.,
Smith, C.,
Spires-Jones, T.,
Pal, S.,
Non-Invasive RF Technique for Detecting Different Stages of
Alzheimer's Disease and Imaging Beta-Amyloid Plaques and Tau Tangles
in the Brain,
MedImg(39), No. 12, December 2020, pp. 4060-4070.
IEEE DOI
2012
Dielectric measurement, Dielectrics, Brain modeling,
Radio frequency, Sensors, Computational modeling, Permittivity,
radio frequency
BibRef
Li, Y.,
Liu, J.,
Tang, Z.,
Lei, B.,
Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional
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MedImg(39), No. 9, September 2020, pp. 2818-2830.
IEEE DOI
2009
Time series analysis, Feature extraction,
Diffusion tensor imaging, Adaptive systems, Topology, Diseases,
spatial-temporal feature
BibRef
Yang, P.,
Zhou, F.,
Ni, D.,
Xu, Y.,
Chen, S.,
Wang, T.,
Lei, B.,
Fused Sparse Network Learning for Longitudinal Analysis of Mild
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Cyber(51), No. 1, January 2021, pp. 233-246.
IEEE DOI
2012
Diseases, Databases, Time series analysis, Imaging, Monitoring,
Brain modeling, Task analysis, Fused sparse network (FSN),
similarity network fusion (SNF)
BibRef
Chen, J.Z.[Jia-Zhou],
Han, G.Q.[Guo-Qiang],
Cai, H.M.[Hong-Min],
Yang, D.F.[De-Fu],
Laurienti, P.J.[Paul J.],
Styner, M.[Martin],
Wu, G.R.[Guo-Rong],
Learning Common Harmonic Waves on Stiefel Manifold:
A New Mathematical Approach for Brain Network Analyses,
MedImg(40), No. 1, January 2021, pp. 419-430.
IEEE DOI
2012
Manifolds, Harmonic analysis, Diseases, Laplace equations,
Optimization, Neuroimaging, Algebra, Brain network,
computer-assisted diagnosis
BibRef
Ganotra, R.[Reema],
Dora, S.[Shirin],
Gupta, S.[Shailender],
Identifying brain regions contributing to Alzheimer's disease using
self regulating particle swarm optimization,
IJIST(31), No. 1, 2021, pp. 106-117.
DOI Link
2102
Alzheimer's disease, gray matter, magnetic resonance imaging,
particle swarm optimization, support vector machines, white matter
BibRef
Lu, L.J.[Lyu-Jian],
Elbeleidy, S.[Saad],
Baker, L.Z.[Lauren Zoe],
Wang, H.[Hua],
Nie, F.P.[Fei-Ping],
Predicting Cognitive Declines Using Longitudinally Enriched
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MedImg(40), No. 3, March 2021, pp. 891-904.
IEEE DOI
2103
BibRef
Earlier: A1, A4, A2, A5, Only:
CVPR20(4826-4835)
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
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Qian, M.Y.[Ming-Yue],
Zhang, Z.T.[Zhao-Ting],
Chen, J.C.[Jie-Chun],
Special Issue Retraction:
Combined mixed Gaussian model with pattern recognition in the automatic
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IET-IPR(17), No. 1, January 2023, pp. 301.
DOI Link
2301
BibRef
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IET-IPR(14), No. 15, 15 December 2020, pp. 3698-3704.
DOI Link
2103
BibRef
Huang, M.Y.[Mei-Yan],
Chen, X.M.[Xiu-Mei],
Yu, Y.W.[Yu-Wei],
Lai, H.R.[Hao-Ran],
Feng, Q.J.[Qian-Jin],
Imaging Genetics Study Based on a Temporal Group Sparse Regression
and Additive Model for Biomarker Detection of Alzheimer's Disease,
MedImg(40), No. 5, May 2021, pp. 1461-1473.
IEEE DOI
2105
Genetics, Diseases, Biological system modeling, Data models,
Brain modeling, Biomedical imaging, Additives, Imaging genetics,
single nucleotide polymorphism
BibRef
Chen, Y.Y.[Yuan-Yuan],
Xia, Y.[Yong],
Iterative sparse and deep learning for accurate diagnosis of
Alzheimer's disease,
PR(116), 2021, pp. 107944.
Elsevier DOI
2106
Alzheimer's disease, Mild cognitive impairment, Deep learning, Sparse regression
BibRef
Wang, M.L.[Mei-Ling],
Shao, W.[Wei],
Hao, X.K.[Xiao-Ke],
Zhang, D.Q.[Dao-Qiang],
Identify Complex Imaging Genetic Patterns via Fusion Self-Expressive
Network Analysis,
MedImg(40), No. 6, June 2021, pp. 1673-1686.
IEEE DOI
2106
Imaging, Correlation, Diseases, Bioinformatics, Image reconstruction,
Genomics, Knowledge engineering, Brain imaging genetics,
Alzheimer's disease
BibRef
Ning, Z.Y.[Zhen-Yuan],
Xiao, Q.[Qing],
Feng, Q.J.[Qian-Jin],
Chen, W.F.[Wu-Fan],
Zhang, Y.[Yu],
Relation-Induced Multi-Modal Shared Representation Learning for
Alzheimer's Disease Diagnosis,
MedImg(40), No. 6, June 2021, pp. 1632-1645.
IEEE DOI
2106
Magnetic resonance imaging, Diseases, Training, Testing, Data models,
Bidirectional control, Alzheimer's disease, Alzheimer's disease,
relational regularization
See also Relation-Aware Shared Representation Learning for Cancer Prognosis Analysis with Auxiliary Clinical Variables and Incomplete Multi-Modality Data.
BibRef
Lao, H.[Huan],
Zhang, X.J.[Xue-Jun],
Tang, Y.Y.[Yan-Yan],
Liang, C.[Chan],
Alzheimer's disease diagnosis based on the visual attention model and
equal-distance ring shape context features,
IET-IPR(15), No. 10, 2021, pp. 2351-2362.
DOI Link
2108
BibRef
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,
MedImg(40), No. 8, August 2021, pp. 2030-2041.
IEEE DOI
2108
Task analysis, Correlation, Diseases, Dictionaries, Encoding,
Neuroimaging, Alzheimer's disease, Multi-task,
multi-resemblance
BibRef
Basheera, S.[Shaik],
Ram, M.S.S.[M Satya Sai],
Deep learning based Alzheimer's disease early diagnosis using T2w
segmented gray matter MRI,
IJIST(31), No. 3, 2021, pp. 1692-1710.
DOI Link
2108
Alzheimer's disease, classification, CNN, deep learning, gray matter
BibRef
Jiao, Z.Q.[Zhu-Qing],
Jiao, T.X.[Ting-Xuan],
Zhang, J.H.[Jia-Hao],
Shi, H.F.[Hai-Feng],
Wu, B.[Bona],
Zhang, Y.D.[Yu-Dong],
Sparse structure deep network embedding for transforming brain
functional network in early mild cognitive impairment classification,
IJIST(31), No. 3, 2021, pp. 1197-1210.
DOI Link
2108
brain functional network,
dilated convolutional neural network (DCNN), transform
BibRef
Bharanidharan, N.,
Rajaguru, H.[Harikumar],
Dementia MRI image classification using transformation technique
based on elephant herding optimization with Randomized Adam method
for updating the hyper-parameters,
IJIST(31), No. 3, 2021, pp. 1221-1245.
DOI Link
2108
dementia, elephant herding optimization,
evolutionary algorithms, MRI, swarm intelligence
BibRef
Karim, R.[Razaul],
Shahrior, A.[Ashef],
Rahman, M.M.[Mohammad Motiur],
Machine learning-based tri-stage classification of Alzheimer's
progressive neurodegenerative disease using PCA and mRMR administered
textural, orientational, and spatial features,
IJIST(31), No. 4, 2021, pp. 2060-2074.
DOI Link
2112
Alzheimer's, feature extraction, machine learning, MRI, VLAD
BibRef
Li, Y.[Yang],
Liu, J.Y.[Jing-Yu],
Jiang, Y.Q.[Yi-Qiao],
Liu, Y.[Yu],
Lei, B.Y.[Bai-Ying],
Virtual Adversarial Training-Based Deep Feature Aggregation Network
From Dynamic Effective Connectivity for MCI Identification,
MedImg(41), No. 1, January 2022, pp. 237-251.
IEEE DOI
2201
Feature extraction, Diseases, Heuristic algorithms, Brain modeling,
Time series analysis, Deep learning, Training,
virtual adversarial training
BibRef
Gopinath, K.[Karthik],
Desrosiers, C.[Christian],
Lombaert, H.[Herve],
Learnable Pooling in Graph Convolutional Networks for Brain Surface
Analysis,
PAMI(44), No. 2, February 2022, pp. 864-876.
IEEE DOI
2201
Brain, Convolution, Geometry, Task analysis, Surface treatment,
Alzheimer's disease, Learnable pooling,
alzheimer classification
BibRef
Shakeri, M.[Mahsa],
Lombaert, H.[Herve],
Tripathi, S.[Shashank],
Kadoury, S.[Samuel],
Deep Spectral-Based Shape Features for Alzheimer's Disease
Classification,
SeSAME16(15-24).
Springer DOI
1703
BibRef
Dinh, T.Q.[Tuan Q.],
Xiong, Y.[Yunyang],
Huang, Z.C.[Zhi-Chun],
Vo, T.[Tien],
Mishra, A.[Akshay],
Kim, W.H.[Won Hwa],
Ravi, S.N.[Sathya N.],
Singh, V.[Vikas],
Performing Group Difference Testing on Graph Structured Data from GANs:
Analysis and Applications in Neuroimaging,
PAMI(44), No. 2, February 2022, pp. 877-889.
IEEE DOI
2201
Is the result the same using GAN generated data and the original real data for
testing.
Statistical analysis, Diseases, Machine learning,
Training data, Training, non-euclidean
BibRef
Eroglu, Y.[Yesim],
Yildirim, M.[Muhammed],
Cinar, A.[Ahmet],
mRMR-based hybrid convolutional neural network model for
classification of Alzheimer's disease on brain magnetic resonance
images,
IJIST(32), No. 2, 2022, pp. 517-527.
DOI Link
2203
Alzheimer's disease, classification, KNN, machine learning, MRI, SVM
BibRef
Babu, G.S.[G. Stalin],
Rao, S.N.T.[S. N. Tirumala],
Rao, R.R.[R. Rajeswara],
Automated assessment for Alzheimer's disease diagnosis from MRI
images: Meta-heuristic assisted deep learning model,
IJIST(32), No. 2, 2022, pp. 544-563.
DOI Link
2203
Alzheimer disease, CG-DU algorithm, DCNN, geometric Haralick,
gray wolf optimizer
BibRef
Dora, L.[Lingraj],
Agrawal, S.[Sanjay],
Panda, R.[Rutuparna],
Abraham, A.[Ajith],
An efficient multiclass classifier for classification of Alzheimer's
disease/mild cognitive impairment/Normal subjects,
IJIST(32), No. 2, 2022, pp. 629-641.
DOI Link
2203
disease classification,
hybrid particle swarm optimization-squirrel search algorithm,
ternary classifier
BibRef
Pohl, T.[Tomáš],
Jakab, M.[Marek],
Benesova, W.[Wanda],
Interpretability of deep neural networks used for the diagnosis of
Alzheimer's disease,
IJIST(32), No. 2, 2022, pp. 673-686.
DOI Link
2203
Alzheimer's disease, deep neural networks, interpretability,
layer-wise relevance propagation, magnetic resonance imaging
BibRef
Shi, Y.[Yuang],
Zu, C.[Chen],
Hong, M.[Mei],
Zhou, L.P.[Lu-Ping],
Wang, L.[Lei],
Wu, X.[Xi],
Zhou, J.[Jiliu],
Zhang, D.[Daoqiang],
Wang, Y.[Yan],
ASMFS: Adaptive-Similarity-Based Multi-Modality Feature Selection for
Classification of Alzheimer's Disease,
PR(126), 2022, pp. 108566.
Elsevier DOI
2204
Multi-modality, Similarity learning, Feature selection, Alzheimer's disease
See also Feature Selection with Kernel Class Separability.
BibRef
Borovkova, M.[Mariia],
Sieryi, O.[Oleksii],
Lopushenko, I.[Ivan],
Kartashkina, N.[Natalia],
Pahnke, J.[Jens],
Bykov, A.[Alexander],
Meglinski, I.[Igor],
Screening of Alzheimer's Disease With Multiwavelength Stokes
Polarimetry in a Mouse Model,
MedImg(41), No. 4, April 2022, pp. 977-982.
IEEE DOI
2204
Brain, Polarimetry, Mice, Measurement by laser beam, Laser beams,
Microscopy, Optical microscopy, Optical polarimetry, scattering,
Stokes vector
BibRef
Lian, C.F.[Chun-Feng],
Liu, M.X.[Ming-Xia],
Pan, Y.S.[Yong-Sheng],
Shen, D.G.[Ding-Gang],
Attention-Guided Hybrid Network for Dementia Diagnosis With
Structural MR Images,
Cyber(52), No. 4, April 2022, pp. 1992-2003.
IEEE DOI
2204
Feature extraction, Brain modeling, Task analysis, Dementia,
Solid modeling, Medical diagnosis, Alzheimer's disease (AD),
weakly supervised localization
BibRef
Perugia, G.[Giulia],
Díaz-Boladeras, M.[Marta],
Català-Mallofré, A.[Andreu],
Barakova, E.I.[Emilia I.],
Rauterberg, M.[Matthias],
ENGAGE-DEM: A Model of Engagement of People With Dementia,
AffCom(13), No. 2, April 2022, pp. 926-943.
IEEE DOI
2206
Dementia, Computational modeling, Mathematical model,
Task analysis, Physiology, Games, Atmospheric measurements,
social agents/robotics
BibRef
Kaur, S.[Swapandeep],
Gupta, S.[Sheifali],
Singh, S.[Swati],
Gupta, I.[Isha],
Detection of Alzheimer's Disease Using Deep Convolutional Neural
Network,
IJIG(22), No. 3 2022, pp. 2140012.
DOI Link
2206
BibRef
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
da Silva, V.F.[Vernon Furtado],
Silva, D.A.S.[Diego Augusto Santos],
Martins, P.C.[Priscila Custódio],
Calomeni, M.R.[Mauricio Rocha],
de Aquino Freire, I.[Ivete],
Militão, A.G.[Angeliete Garcês],
Borges, C.J.[Célio José],
de Barros-Vilela-Junior, G.[Guanis],
de Moraes, M.A.[Mario Antônio],
Silva, A.J.R.M.[Antônio José Rocha Martins],
de Almeida-Marinho, D.[Daniel],
Ribeiro, D.E.C.[Domingos Edno Castro],
Ribeiro, E.C.[Edinilson Castro],
Valentim-Silva, J.R.[João Rafael],
Effect of physical exercise and noninvasive brain stimulation on
cognition and dementia of elderly people with frailty: A randomized
study,
IJIST(32), No. 6, 2022, pp. 1941-1952.
DOI Link
2212
aging, brain stimulation, inhibitory control, qEEG, working memory
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
BibRef
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
BibRef
Cai, H.J.[Hong-Jie],
Gao, Y.[Yue],
Liu, M.H.[Man-Hua],
Graph Transformer Geometric Learning of Brain Networks Using
Multimodal MR Images for Brain Age Estimation,
MedImg(42), No. 2, February 2023, pp. 456-466.
IEEE DOI
2302
Estimation, Diffusion tensor imaging, Transformers, Aging,
Brain modeling, Data models, Convolutional neural networks,
Alzheimer's disease
BibRef
Song, X.G.[Xue-Gang],
Zhou, F.[Feng],
Frangi, A.F.[Alejandro F.],
Cao, J.[Jiuwen],
Xiao, X.H.[Xiao-Hua],
Lei, Y.[Yi],
Wang, T.F.[Tian-Fu],
Lei, B.[Baiying],
Multicenter and Multichannel Pooling GCN for Early AD Diagnosis Based
on Dual-Modality Fused Brain Network,
MedImg(42), No. 2, February 2023, pp. 354-367.
IEEE DOI
2302
Diseases, Task analysis, Diffusion tensor imaging, Training,
Neuroimaging, Filtering theory, dual-modality fusion
BibRef
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.[Jiashuang],
Wang, H.[Heyang],
Xu, R.[Ruting],
Shao, W.[Wei],
Zhang, D.[Daoqiang],
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.
DOI Link
2306
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,
AI-Care22(154-164).
Springer DOI
2208
BibRef
Ostertag, C.[Cecilia],
Beurton-Aimar, M.[Marie],
Visani, M.[Muriel],
Urruty, T.[Thierry],
Bertet, K.[Karell],
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
Bastos, J.[José],
Silva, F.[Filipe],
Georgieva, P.[Petia],
Deep Learning for Diagnosis of Alzheimer's Disease with FDG-PET
Neuroimaging,
IbPRIA22(95-107).
Springer DOI
2205
BibRef
Parziale, A.[Antonio],
Cioppa, A.D.[Antonio Della],
Marcelli, A.[Angelo],
Investigating One-Class Classifiers to Diagnose Alzheimer's Disease
from Handwriting,
CIAP22(I:111-123).
Springer DOI
2205
BibRef
Fontanella, F.[Francesco],
Pinelli, S.[Sonia],
Babiloni, C.[Claudio],
Lizio, R.[Roberta],
del Percio, C.[Claudio],
Lopez, S.[Susanna],
Noce, G.[Giuseppe],
Giubilei, F.[Franco],
Stocchi, F.[Fabrizio],
Frisoni, G.B.[Giovanni B.],
Nobili, F.[Flavio],
Ferri, R.[Raffaele],
d'Alessandro, T.[Tiziana],
Cilia, N.D.[Nicole Dalia],
de Stefano, C.[Claudio],
Machine Learning to Predict Cognitive Decline of Patients with
Alzheimer's Disease Using EEG Markers: A Preliminary Study,
CIAP22(I:137-147).
Springer DOI
2205
BibRef
Amini, M.[Morteza],
Mahmoodi, T.[Tayeb],
Sajedi, H.[Hedieh],
Mirzaea, S.[Sayeh],
Detection of Cortical Demantia in MRI Images Using Convolutional
Autoencoder Neural Network,
IPRIA21(1-3)
IEEE DOI
2201
Image analysis, Magnetic resonance imaging, Brain mapping,
Pattern recognition, Convolutional neural networks,
Classification
BibRef
Ayyar, M.P.[Meghna P.],
Benois-Pineau, J.[Jenny],
Zemmari, A.[Akka],
Catheline, G.[Gwenaelle],
Explaining 3D CNNs for Alzheimer's Disease Classification on sMRI
Images with Multiple ROIs,
ICIP21(284-288)
IEEE DOI
2201
Heating systems, Deep learning, Correlation,
Magnetic resonance imaging, Image processing,
Deep Learning understanding
BibRef
Liu, C.[Chao],
Yang, X.D.[Xiao-Dong],
Chong, D.[Dading],
Wang, W.[Wenwu],
Li, L.[Liang],
Enhancing Alzheimer's Disease Diagnosis via Hierarchical 3D-FCN with
Multi-Modal Features,
ICIP21(304-308)
IEEE DOI
2201
Training, Neurological diseases, Sociology, Senior citizens,
Feature extraction, Medical diagnosis,
Multi-layer perceptron
BibRef
Zhang, L.[Lin],
Xin, B.[Bowen],
Yan, S.Z.[Shao-Zhen],
Zheng, C.[Chaoiie],
Zhou, Y.[Yun],
Lu, J.[Jie],
Wang, X.Y.[Xiu-Ying],
Multi-stratification feature selection for diagnostic analysis of
Alzheimer's disease,
DICTA21(01-07)
IEEE DOI
2201
Neuroimaging, Brain, Magnetic resonance imaging, Digital images,
Feature extraction, Alzheimer's disease, Task analysis
BibRef
Cilia, N.D.[Nicole Dalia],
de Stefano, C.[Claudio],
Marrocco, C.[Claudio],
Fontanella, F.[Francesco],
Molinara, M.[Mario],
di Freca, A.S.[Alessandra Scotto],
Deep Transfer Learning for Alzheimer's disease detection,
ICPR21(9904-9911)
IEEE DOI
2105
Decision support systems, Shape, Image color analysis,
Transfer learning, Neural networks, Feature extraction, Pattern recognition
BibRef
Yu, F.[Fei],
Zhao, B.Q.[Bao-Qi],
Ge, Q.Q.[Qing-Qing],
Zhang, Z.J.[Zhi-Jie],
Sun, J.M.[Jun-Mei],
Li, X.M.[Xiu-Mei],
A Lightweight Spatial Attention Module with Adaptive Receptive Fields
in 3d Convolutional Neural Network for Alzheimer's Disease
Classification,
AIHA20(575-586).
Springer DOI
2103
BibRef
Dentamaro, V.[Vincenzo],
Impedovo, D.[Donato],
Pirlo, G.[Giuseppe],
An Analysis of Tasks and Features for Neuro-degenerative Disease
Assessment by Handwriting,
AIHA20(536-545).
Springer DOI
2103
BibRef
Cilia, N.D.[Nicole Dalia],
de Stefano, C.[Claudio],
Fontanella, F.[Francesco],
di Freca, A.S.[Alessandra Scotto],
Handwriting-based Classifier Combination for Cognitive Impairment
Prediction,
AIHA20(587-599).
Springer DOI
2103
BibRef
de Gregorio, G.[Giuseppe],
Desiato, D.[Domenico],
Marcelli, A.[Angelo],
Polese, G.[Giuseppe],
A Multi Classifier Approach for Supporting Alzheimer's Diagnosis Based
on Handwriting Analysis,
AIHA20(559-574).
Springer DOI
2103
BibRef
Ebrahimi, A.,
Luo, S.,
Chiong, R.,
Introducing Transfer Leaming to 3D ResNet-18 for Alzheimer's Disease
Detection on MRI Images,
IVCNZ20(1-6)
IEEE DOI
2012
Training, Solid modeling,
Magnetic resonance imaging, Computational modeling, Taguchi
BibRef
Zhou, L.,
Zhang, L.,
Bai, X.,
Zhou, J.,
Matrix Classifier On Dynamic Functional Connectivity For Mci
Identification,
ICIP20(325-329)
IEEE DOI
2011
Feature extraction, Correlation, Dementia,
Spatiotemporal phenomena, Training, Alzheimer's disease (AD),
support matrix machines (SMM)
BibRef
Miller, M.I.[Michael I.],
Tward, D.J.[Daniel J.],
Trouvé, A.[Alain],
Coarse-to-Fine Hamiltonian Dynamics of Hierarchical Flows in
Computational Anatomy,
Diff-CVML20(3760-3765)
IEEE DOI
2008
Results on Alzheimer's.
Shape, Kernel,
Mathematical model, Shape measurement, Atmospheric measurements
BibRef
Slapnicar, G.,
Dovgan, E.,
Cuk, P.,
Lustrek, M.,
Contact-Free Monitoring of Physiological Parameters in People With
Profound Intellectual and Multiple Disabilities,
CVPM19(1664-1672)
IEEE DOI
2004
Videos, Skin, Biomedical monitoring, Heart rate, Databases, Blood,
Physiology, physiological signals, PIMD, deep learning, LSTM, rPPG,
video cameras
BibRef
Zhang, Y.[Yanfu],
Zhan, L.[Liang],
Thompson, P.M.[Paul M.],
Huang, H.[Heng],
Biological Knowledge Guided Deep Neural Network for Brain
Genotype-phenotype Association Study,
MBIA19(84-92).
Springer DOI
1912
BibRef
Tang, H.T.[Hao-Teng],
Guo, L.[Lei],
Dennis, E.[Emily],
Thompson, P.M.[Paul M.],
Huang, H.[Heng],
Ajilore, O.[Olusola],
Leow, A.D.[Alex D.],
Zhan, L.[Liang],
Classifying Stages of Mild Cognitive Impairment via Augmented Graph
Embedding,
MBIA19(30-38).
Springer DOI
1912
BibRef
Peng, B.[Bo],
Ren, Z.Y.[Zhi-Yun],
Yao, X.H.[Xiao-Hui],
Liu, K.[Kefei],
Saykin, A.J.[Andrew J.],
Shen, L.[Li],
Ning, X.[Xia],
Prioritizing Amyloid Imaging Biomarkers in Alzheimer's Disease via
Learning to Rank,
MBIA19(139-148).
Springer DOI
1912
BibRef
Wegmayr, V.[Viktor],
Hörold, M.[Maurice],
Buhmann, J.M.[Joachim M.],
Generative Aging of Brain MR-Images and Prediction of Alzheimer
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GCPR19(247-260).
Springer DOI
1911
BibRef
Ge, C.,
Qu, Q.,
Gu, I.Y.,
Store Jakola, A.,
Multiscale Deep Convolutional Networks for Characterization and
Detection of Alzheimer's Disease Using MR images,
ICIP19(789-793)
IEEE DOI
1910
Alzheimer's disease detection, MR images, multiscale features,
multiscale CNN, feature fusion and enhancement
BibRef
Dentamaro, V.[Vincenzo],
Impedovo, D.[Donato],
Pirlo, G.[Giuseppe],
Real-Time Neurodegenerative Disease Video Classification with Severity
Prediction,
CIAP19(II:618-628).
Springer DOI
1909
BibRef
Cilia, N.D.[Nicole Dalia],
de Stefano, C.[Claudio],
Fontanella, F.[Francesco],
Molinara, M.[Mario],
di Freca, A.S.[Alessandra Scotto],
Using Handwriting Features to Characterize Cognitive Impairment,
CIAP19(II:683-693).
Springer DOI
1909
BibRef
Plocharski, M.[Maciej],
Østergaard, L.R.[Lasse Riis],
Initiative, T.A.D.N.[The Alzheimers Disease Neuroimaging],
Sulcal and Cortical Features for Classification of Alzheimer's Disease
and Mild Cognitive Impairment,
SCIA19(427-438).
Springer DOI
1906
BibRef
Gorodissky, O.A.,
Sharon, A.,
Danov, A.,
Friedman, A.,
Raviv, T.R.,
Symmetry-Based Analysis of Diffusion MRI for the Detection of Brain
Impairments,
ICIP18(376-379)
IEEE DOI
1809
Isosurfaces, Diffusion tensor imaging, Brain injuries, Symmetry,
Shape dissimilarity, MRI, Fractional Anisotropy, Mean Diffusion,
Contact Sport players
BibRef
Pan, X.,
Adel, M.,
Fossati, C.,
Gaidon, T.,
Guedj, E.,
Alzheimer's Disease Diagnosis with FDG-PET Brain Images By Using
Multi-Level Features,
ICIP18(366-370)
IEEE DOI
1809
Feature extraction, Dementia, Magnetic resonance imaging,
Positron emission tomography, Symmetric matrices,
Alzheimer's Disease
BibRef
Zuanon, R.[Rachel],
de Faria, B.A.C.[Barbara Alves Cardoso],
Landscape Design and Neuroscience Cooperation: Contributions to the
Non-pharmacological Treatment of Alzheimer's Disease,
DHM18(353-374).
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
Mehta, R.,
Chakraborty, R.,
Singh, V.,
Xiong, Y.,
Scaling Recurrent Models via Orthogonal Approximations in Tensor
Trains,
ICCV19(10570-10578)
IEEE DOI
2004
Tensile stress, Manifolds, Computational modeling, Brain modeling,
Data models, Solid modeling
BibRef
Chakraborty, R.,
Vemuri, B.C.,
Statistical analysis of longitudinal data and applications to
neuro-imaging,
ICIP17(211-214)
IEEE DOI
1803
Dementia, Electronics packaging, Frequency modulation, Manifolds,
Measurement, Statistical analysis, Trajectory,
Trajectories
BibRef
El-Gamal, F.E.Z.A.,
Elmogy, M.M.,
Atwan, A.,
Ghazal, M.,
Barnes, G.N.,
Hajjdiab, H.,
Keynton, R.,
El-Baz, A.S.,
Significant Region-Based Framework for Early Diagnosis of Alzheimer's
Disease Using11C PiB-PET Scans,
ICPR18(2989-2994)
IEEE DOI
1812
Diseases, Feature extraction, Labeling, Statistical analysis,
Support vector machines, Positron emission tomography, Standardization
BibRef
El-Gamal, F.E.Z.A.,
Elmogy, M.M.,
Ghazal, M.,
Atwan, A.,
Barnes, G.N.,
Casanova, M.F.,
Keynton, R.,
El-Baz, A.S.,
A novel CAD system for local and global early diagnosis of
Alzheimer's disease based on PIB-PET scans,
ICIP17(3270-3274)
IEEE DOI
1803
Brain, Databases, Diseases, Feature extraction, Noise reduction,
Probabilistic logic, Support vector machines,
PIB-PET
BibRef
Shams-Baboli, A.,
Ezoji, M.,
A Zernike moment based method for classification of Alzheimer's
disease from structural MRI,
IPRIA17(38-43)
IEEE DOI
1712
backpropagation, biomedical MRI, diseases, feature extraction,
image classification, medical image processing, neural nets,
mild cognitive impairment
BibRef
Konukoglu, E.[Ender],
Glocker, B.[Ben],
Constructing Subject- and Disease-Specific Effect Maps:
Application to Neurodegenerative Diseases,
MCV16(3-13).
Springer DOI
1711
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.W.[Jun-Wei],
Huang, Q.[Qiu],
Prediction of MCI to AD conversion using Laplace Eigenmaps learned
from FDG and MRI images of AD patients and healthy controls,
ICIVC17(660-664)
IEEE DOI
1708
Diseases, Hippocampus, Image reconstruction,
Magnetic resonance imaging, Prognostics and health management,
Proteins, Support vector machines, alzheimers, image processing,
manifold, medical image, prediction
BibRef
Wang, J.J.[Jian-Jia],
Wilson, R.C.[Richard C.],
Hancock, E.R.[Edwin R.],
Quantum Edge Entropy for Alzheimer's Disease Analysis,
SSSPR18(449-459).
Springer DOI
1810
BibRef
Earlier:
Detecting Alzheimer's Disease Using Directed Graphs,
GbRPR17(94-104).
Springer DOI
1706
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
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
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.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
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],
Initiative, T.A.D.N.[The Alzheimers Disease Neuroimaging],
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],
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
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
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ICPR12(153-156).
WWW Link.
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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
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Silveira, M.[Margarida],
Marques, J.S.[Jorge S.],
Alternative feature extraction methods in 3D brain image-based
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ICIP12(1237-1240).
IEEE DOI
1302
BibRef
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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
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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
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MBIA12(18-28).
Springer DOI
1210
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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
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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],
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Davatzikos, C.[Christos],
JointMMCC: Joint Maximum-Margin Classification and Clustering of
Imaging Data,
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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
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MLMI11(159-166).
Springer DOI
1109
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Wyatt, C.[Chris],
An automatic unsupervised classification of MR images in Alzheimer's
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CVPR10(2910-2917).
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1006
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Silveira, M.[Margarida],
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Boosting Alzheimer Disease Diagnosis Using PET Images,
ICPR10(2556-2559).
IEEE DOI
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain, Parkinson's Disease .