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1309
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Brain modeling
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1502
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Earlier:
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Alzheimer's disease
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1606
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Earlier:
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1511
Alzheimer's disease
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1804
biomedical MRI, brain, cognition, diseases, graph theory,
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1511
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1211
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1611
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MLMI15(255-262).
Springer DOI
1511
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Earlier: A1, A2, A6, Only:
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1410
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MLMI16(77-85).
Springer DOI
1611
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Springer DOI
1611
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1109
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MBIA12(175-184).
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.
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Li, Z.,
Suk, H.I.,
Shen, D.,
Li, L.,
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MedImg(35), No. 8, August 2016, pp. 1927-1936.
IEEE DOI
1608
Alzheimer's disease
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Zhou, L.P.[Lu-Ping],
Wang, L.[Lei],
Liu, L.Q.[Ling-Qiao],
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Learning Discriminative Bayesian Networks from High-Dimensional
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PAMI(38), No. 11, November 2016, pp. 2269-2283.
IEEE DOI
1610
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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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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],
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Smith, C.D.[Charles D.],
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PR(63), No. 1, 2017, pp. 487-498.
Elsevier DOI
1612
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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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DOI Link
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Guerrero, R.,
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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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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],
Castiglioni, I.[Isabella],
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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],
Kwon, G.R.[Goo-Rak],
Initiative, T.A.D.N.[The Alzheimer's Disease Neuroimaging],
Alzheimer disease classification using KPCA, LDA, and multi-kernel
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FreeSurfer, CIVET, KPCA, PCA, LDA, MK-SVM
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Dadar, M.,
Pascoal, T.A.,
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Misquitta, K.,
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1708
Alzheimer's disease, Image segmentation, Lesions,
Magnetic resonance imaging, Robustness, Alzheimer's disease,
White matter hyperintensities, aging, segmentation
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Shan, X.F.[Xuan-Feng],
Zhao, D.[Dazhe],
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1708
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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Sensing Technologies for Monitoring Serious Mental Illnesses,
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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
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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],
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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
images for Alzheimer's disease diagnosis,
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
prediction,
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.[Xiaoli],
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
BibRef
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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PRL(136), 2020, pp. 168-174.
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
Lu, L.,
Wang, H.,
Elbeleidy, S.,
Nie, F.,
Predicting Cognitive Declines Using Longitudinally Enriched
Representations for Imaging Biomarkers,
CVPR20(4826-4835)
IEEE DOI
2008
Neuroimaging, Biomarkers, Dementia, Brain modeling, Biomedical imaging
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
Progression,
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], And the Alzheimer's Disease Neuroimaging Initiative,
Sulcal and Cortical Features for Classification of Alzheimer's Disease
and Mild Cognitive Impairment,
SCIA19(427-438).
Springer DOI
1906
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, Three-dimensional displays
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.,
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain, Parkinson's Disease .