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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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biomedical MRI, brain, cognition, diseases, graph theory,
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Springer DOI
1511
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Earlier: A1, A2, A6, Only:
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Springer DOI
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1611
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1109
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Springer DOI
1210
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IEEE DOI
1106
T1-weighted MRI for mild cognitive impairment.
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Li, Z.,
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Shen, D.,
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1608
Alzheimer's disease
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Zhou, L.P.[Lu-Ping],
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PAMI(38), No. 11, November 2016, pp. 2269-2283.
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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)
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1409
Graphical LASSO
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Elsevier DOI
1612
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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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PR(63), No. 1, 2017, pp. 487-498.
Elsevier DOI
1612
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And:
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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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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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Munsell, B.C.,
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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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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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Misquitta, K.,
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Alzheimer's disease, Image segmentation, Lesions,
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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,
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sensing
BibRef
Alvarez, F.,
Popa, M.,
Solachidis, V.,
Hernández-Peñaloza, G.,
Belmonte-Hernández, A.,
Asteriadis, S.,
Vretos, N.,
Quintana, M.,
Theodoridis, T.,
Dotti, D.,
Daras, P.,
Behavior Analysis through Multimodal Sensing for Care of Parkinson's
and Alzheimer's Patients,
MultMedMag(25), No. 1, January 2018, pp. 14-25.
IEEE DOI
1804
Alzheimer's disease, Behavioral sciences, Biomedical imaging,
Calibration, Feature extraction, Patient monitoring, Sensors,
wireless sensor networks
BibRef
Alvarez, F.,
Popa, M.,
Vretos, N.,
Belmonte-Hernández, A.,
Asteriadis, S.,
Solachidis, V.,
Mariscal, T.,
Dotti, D.,
Daras, P.,
Multimodal monitoring of Parkinson's and Alzheimer's patients using
the ICT4LIFE platform,
AVSS17(1-6)
IEEE DOI
1806
Internet of Things, diseases, feature extraction,
medical computing, patient monitoring, sensor fusion,
Wireless sensor networks
BibRef
Strickland, E.,
The digital fingerprints of brain disorders,
Spectrum(55), No. 5, May 2018, pp. 12-13.
IEEE DOI
1805
[News]
BibRef
Asim, Y.[Yousra],
Raza, B.[Basit],
Malik, A.K.[Ahmad Kamran],
Rathore, S.[Saima],
Hussain, L.[Lal],
Iftikhar, M.A.[Mohammad Aksam],
A multi-modal, multi-atlas-based approach for Alzheimer detection via
machine learning,
IJIST(28), No. 2, 2018, pp. 113-123.
WWW Link.
1806
BibRef
Kahindo, C.,
El-Yacoubi, M.A.,
Garcia-Salicetti, S.,
Rigaud, A.,
Cristancho-Lacroix, V.,
Characterizing Early-Stage Alzheimer Through Spatiotemporal Dynamics
of Handwriting,
SPLetters(25), No. 8, August 2018, pp. 1136-1140.
IEEE DOI
1808
Bayes methods, diseases, feature extraction,
handwritten character recognition, neurophysiology,
probabilistic modeling
BibRef
El-Yacoubi, M.A.[Mounîm A.],
Garcia-Salicetti, S.[Sonia],
Kahindo, C.[Christian],
Rigaud, A.S.[Anne-Sophie],
Cristancho-Lacroix, V.[Victoria],
From aging to early-stage Alzheimer's: Uncovering handwriting
multimodal behaviors by semi-supervised learning and sequential
representation learning,
PR(86), 2019, pp. 112-133.
Elsevier DOI
1811
Online handwriting, Mild Cognitive Impairment, Aging,
Unsupervised & semi-supervised learning, Temporal representation learning
BibRef
Mishra, S.[Shiwangi],
Beheshti, I.[Iman],
Khanna, P.[Pritee],
A statistical region selection and randomized volumetric features
selection framework for early detection of Alzheimer's disease,
IJIST(28), No. 4, December 2018, pp. 302-314.
WWW Link.
1811
Alzheimer's Disease Neuroimaging Initiative
BibRef
Baumgartner, C.F.,
Koch, L.M.,
Tezcan, K.C.,
Ang, J.X.,
Visual Feature Attribution Using Wasserstein GANs,
CVPR18(8309-8319)
IEEE DOI
1812
Visualization, Biomedical imaging,
Alzheimer's disease, Neural networks, Neuroimaging
BibRef
Islam, J.,
Zhang, Y.,
Early Diagnosis of Alzheimer's Disease:
A Neuroimaging Study with Deep Learning Architectures,
WiCV18(1962-19622)
IEEE DOI
1812
Alzheimer's disease, Magnetic resonance imaging, Brain modeling,
Training, Medical diagnosis
BibRef
Zhang, Y.[Yu],
Zhang, H.[Han],
Chen, X.B.[Xiao-Bo],
Liu, M.X.[Ming-Xia],
Zhu, X.F.[Xiao-Feng],
Lee, S.W.[Seong-Whan],
Shen, D.G.[Ding-Gang],
Strength and similarity guided group-level brain functional network
construction for MCI diagnosis,
PR(88), 2019, pp. 421-430.
Elsevier DOI
1901
Alzheimers disease, Mild cognitive impairment,
Resting-state functional magnetic resonance imaging (rs-fMRI),
Diagnosis
BibRef
Peng, J.[Jialin],
Zhu, X.F.[Xiao-Feng],
Wang, Y.[Ye],
An, L.[Le],
Shen, D.G.[Ding-Gang],
Structured sparsity regularized multiple kernel learning for
Alzheimer's disease diagnosis,
PR(88), 2019, pp. 370-382.
Elsevier DOI
1901
Structured sparsity, Multimodal features,
Multiple kernel learning, Feature selection, Alzheimer's disease diagnosis
BibRef
Zhu, Y.,
Zhu, X.,
Kim, M.,
Yan, J.,
Kaufer, D.,
Wu, G.,
Dynamic Hyper-Graph Inference Framework for Computer-Assisted
Diagnosis of Neurodegenerative Diseases,
MedImg(38), No. 2, February 2019, pp. 608-616.
IEEE DOI
1902
Imaging, Training, Testing, Diseases, Data models, Training data,
Neuroimaging, Hyper-graph learning, computer assisted diagnosis,
neurodegenerative disease
BibRef
Singh, S.[Sneha],
Anand, R.S.,
Multimodal neurological image fusion based on adaptive biological
inspired neural model in nonsubsampled Shearlet domain,
IJIST(29), No. 1, March 2019, pp. 50-64.
WWW Link.
1902
BibRef
Karami, V.[Vania],
Nittari, G.[Giulio],
Amenta, F.[Francesco],
Neuroimaging computer-aided diagnosis systems for Alzheimer's disease,
IJIST(29), No. 1, March 2019, pp. 83-94.
WWW Link.
1902
BibRef
He, M.,
Baker, S.L.,
Shah, V.D.,
Lockhart, S.N.,
Jagust, W.J.,
Scan-Time Corrections for 80-100-min Standardizetd Uptake Volume
Ratios to Measure the 18F-AV-1451 Tracer for Tau Imaging,
MedImg(38), No. 3, March 2019, pp. 697-709.
IEEE DOI
1903
Dementia, Image reconstruction, Interpolation, Training,
Magnetic resonance imaging, Tau, PET Imaging, SUVR, scan-time error,
Alzheimer's disease
BibRef
Yu, R.P.[Ren-Ping],
Qiao, L.S.[Li-Shan],
Chen, M.M.[Ming-Ming],
Lee, S.W.[Seong-Whan],
Fei, X.[Xuan],
Shen, D.G.[Ding-Gang],
Weighted graph regularized sparse brain network construction for MCI
identification,
PR(90), 2019, pp. 220-231.
Elsevier DOI
1903
Graph Laplacian regularization, Sparse representation,
Brain functional network, Mild cognitive impairment (MCI)
BibRef
De Stefano, C.[Claudio],
Fontanella, F.[Francesco],
Impedovo, D.[Donato],
Pirlo, G.[Giuseppe],
di Freca, A.S.[Alessandra Scotto],
Handwriting analysis to support neurodegenerative diseases diagnosis:
A review,
PRL(121), 2019, pp. 37-45.
Elsevier DOI
1904
BibRef
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
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
Chakraborty, R.,
Vemuri, B.C.,
Statistical analysis of longitudinal data and applications to
neuro-imaging,
ICIP17(211-214)
IEEE DOI
1803
Dementia, Electronics packaging, Frequency modulation, Manifolds,
Measurement, Statistical analysis, Trajectory,
Trajectories
BibRef
El-Gamal, F.E.Z.A.,
Elmogy, M.M.,
Atwan, A.,
Ghazal, M.,
Barnes, G.N.,
Hajjdiab, H.,
Keynton, R.,
El-Baz, A.S.,
Significant Region-Based Framework for Early Diagnosis of Alzheimer's
Disease Using11C PiB-PET Scans,
ICPR18(2989-2994)
IEEE DOI
1812
Diseases, Feature extraction, Labeling, Statistical analysis,
Support vector machines, Positron emission tomography, Standardization
BibRef
El-Gamal, F.E.Z.A.,
Elmogy, M.M.,
Ghazal, M.,
Atwan, A.,
Barnes, G.N.,
Casanova, M.F.,
Keynton, R.,
El-Baz, A.S.,
A novel CAD system for local and global early diagnosis of
Alzheimer's disease based on PIB-PET scans,
ICIP17(3270-3274)
IEEE DOI
1803
Brain, Databases, Diseases, Feature extraction, Noise reduction,
Probabilistic logic, Support vector machines,
PIB-PET
BibRef
Shams-Baboli, A.,
Ezoji, M.,
A Zernike moment based method for classification of Alzheimer's
disease from structural MRI,
IPRIA17(38-43)
IEEE DOI
1712
backpropagation, biomedical MRI, diseases, feature extraction,
image classification, medical image processing, neural nets,
mild cognitive impairment
BibRef
Konukoglu, E.[Ender],
Glocker, B.[Ben],
Constructing Subject- and Disease-Specific Effect Maps:
Application to Neurodegenerative Diseases,
MCV16(3-13).
Springer DOI
1711
BibRef
Zhang, J.[Jun],
Liu, M.[Mingxia],
An, L.[Le],
Gao, Y.[Yaozong],
Shen, D.G.[Ding-Gang],
Landmark-Based Alzheimer's Disease Diagnosis Using Longitudinal
Structural MR Images,
MCV16(35-45).
Springer DOI
1711
BibRef
Endo, T.,
Ukita, N.,
Tanaka, H.,
Hagita, N.,
Nakamura, S.,
Adachi, H.,
Ikeda, M.,
Kazui, H.,
Kudo, T.,
Initial response time measurement in eye movement for dementia
screening test,
MVA17(262-265)
DOI Link
1708
Cameras, Dementia, Facial features, Feature extraction, Iris, Time, factors
BibRef
Ding, J.[Junwei],
Huang, Q.[Qiu],
Prediction of MCI to AD conversion using Laplace Eigenmaps learned
from FDG and MRI images of AD patients and healthy controls,
ICIVC17(660-664)
IEEE DOI
1708
Diseases, Hippocampus, Image reconstruction,
Magnetic resonance imaging, Prognostics and health management,
Proteins, Support vector machines, alzheimers, image processing,
manifold, medical image, prediction
BibRef
Shukla, P.,
Gupta, T.,
Saini, A.,
Singh, P.,
Balasubramanian, R.,
A Deep Learning Frame-Work for Recognizing Developmental Disorders,
WACV17(705-714)
IEEE DOI
1609
Atmospheric modeling, Autism, Computer vision, Face, Genetics,
Neural networks, Support, vector, machines
BibRef
Wang, J.J.[Jian-Jia],
Wilson, R.C.[Richard C.],
Hancock, E.R.[Edwin R.],
Quantum Edge Entropy for Alzheimer's Disease Analysis,
SSSPR18(449-459).
Springer DOI
1810
BibRef
Earlier:
Detecting Alzheimer's Disease Using Directed Graphs,
GbRPR17(94-104).
Springer DOI
1706
See also Network Edge Entropy from Maxwell-Boltzmann Statistics.
BibRef
Bernardes, R.[Rui],
Silva, G.[Gilberto],
Chiquita, S.[Samuel],
Serranho, P.[Pedro],
Ambrósio, A.F.[António Francisco],
Retinal Biomarkers of Alzheimer's Disease: Insights from Transgenic
Mouse Models,
ICIAR17(541-550).
Springer DOI
1706
BibRef
Kumar, K.,
Desrosiers, C.,
Chaddad, A.,
Toews, M.,
Spatially constrained sparse regression for the data-driven discovery
of Neuroimaging biomarkers,
ICPR16(2162-2167)
IEEE DOI
1705
Alzheimer's disease, Biomarkers, Brain modeling, Data models,
Databases, Neuroimaging
BibRef
Montenegro, J.M.F.[Juan Manuel Fernandez],
Villarini, B.[Barbara],
Gkelias, A.[Athanasios],
Argyriou, V.[Vasileios],
Cognitive Behaviour Analysis Based on Facial Information Using Depth
Sensors,
UHA3DS16(15-28).
Springer DOI
1806
BibRef
Montenegro, J.M.F.[Juan Manuel Fernandez],
Gkelias, A.[Athanasios],
Argyriou, V.[Vasileios],
Emotion Understanding Using Multimodal Information Based on
Autobiographical Memories for Alzheimer's Patients,
Assist16(I: 252-268).
Springer DOI
1704
BibRef
Cury, C.[Claire],
Lorenzi, M.[Marco],
Cash, D.[David],
Nicholas, J.M.[Jennifer M.],
Routier, A.[Alexandre],
Rohrer, J.[Jonathan],
Ourselin, S.[Sebastien],
Durrleman, S.[Stanley],
Modat, M.[Marc],
Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of
Early Changes in Neurodegenerative Disease,
SeSAME16(63-75).
Springer DOI
1703
BibRef
Rudas, J.[Jorge],
Martínez, D.[Darwin],
Demertzi, A.[Athena],
Di Perri, C.[Carol],
Heine, L.[Lizette],
Tshibanda, L.[Luaba],
Soddu, A.[Andrea],
Multivariate Functional Network Connectivity for Disorders of
Consciousness,
CIARP16(434-442).
Springer DOI
1703
BibRef
Shakeri, M.[Mahsa],
Lombaert, H.[Herve],
Tripathi, S.[Shashank],
Kadoury, S.[Samuel],
Deep Spectral-Based Shape Features for Alzheimer's Disease
Classification,
SeSAME16(15-24).
Springer DOI
1703
BibRef
Bhatkoti, P.,
Paul, M.,
Early diagnosis of Alzheimer's disease: A multi-class deep learning
framework with modified k-sparse autoencoder classification,
ICVNZ16(1-5)
IEEE DOI
1701
Alzheimer's disease
BibRef
Joshi, S.H.[Shantanu H.],
Xie, Q.[Qian],
Kurtek, S.[Sebastian],
Srivastava, A.[Anuj],
Laga, H.[Hamid],
Surface Shape Morphometry for Hippocampal Modeling in Alzheimer's
Disease,
DICTA16(1-8)
IEEE DOI
1701
Diseases
BibRef
Aderghal, K.[Karim],
Boissenin, M.[Manuel],
Benois-Pineau, J.[Jenny],
Catheline, G.[Gwenaëlle],
Afdel, K.[Karim],
Classification of sMRI for AD Diagnosis with Convolutional Neuronal
Networks: A Pilot 2-D+ epsilon Study on ADNI,
MMMod17(I: 690-701).
Springer DOI
1701
BibRef
Tang, X.,
Albert, M.,
Miller, M.I.[Michael I.],
Younes, L.[Laurent],
Change Point Estimation of the Hippocampal Volumes in Alzheimer's
Disease,
CRV16(358-361)
IEEE DOI
1612
Alzheimer's disease
BibRef
Kim, W.H.[Won Hwa],
Kim, H.J.[Hyunwoo J.],
Adluru, N.[Nagesh],
Singh, V.[Vikas],
Latent Variable Graphical Model Selection Using Harmonic Analysis:
Applications to the Human Connectome Project (HCP),
CVPR16(2443-2451)
IEEE DOI
1612
BibRef
Yang, X.[Xi],
Jin, Y.[Yan],
Chen, X.B.[Xiao-Bo],
Zhang, H.[Han],
Li, G.[Gang],
Shen, D.G.[Ding-Gang],
Functional Connectivity Network Fusion with Dynamic Thresholding for
MCI Diagnosis,
MLMI16(246-253).
Springer DOI
1611
BibRef
Zheng, X.[Xiao],
Shi, J.[Jun],
Ying, S.[Shihui],
Zhang, Q.[Qi],
Li, Y.[Yan],
Improving Single-Modal Neuroimaging Based Diagnosis of Brain Disorders
via Boosted Privileged Information Learning Framework,
MLMI16(95-103).
Springer DOI
1611
BibRef
Zu, C.[Chen],
Gao, Y.[Yue],
Munsell, B.[Brent],
Kim, M.J.[Min-Jeong],
Peng, Z.[Ziwen],
Zhu, Y.Y.[Ying-Ying],
Gao, W.[Wei],
Zhang, D.Q.[Dao-Qiang],
Shen, D.G.[Ding-Gang],
Wu, G.R.[Guo-Rong],
Identifying High Order Brain Connectome Biomarkers via Learning on
Hypergraph,
MLMI16(1-9).
Springer DOI
1611
BibRef
Hosseini-Asl, E.,
Keynton, R.,
El-Baz, A.,
Alzheimer's disease diagnostics by adaptation of 3D convolutional
network,
ICIP16(126-130)
IEEE DOI
1610
Convolution
BibRef
Rabeh, A.B.,
Benzarti, F.,
Amiri, H.,
Diagnosis of Alzheimer Diseases in Early Step Using SVM (Support
Vector Machine),
CGiV16(364-367)
IEEE DOI
1608
diseases
BibRef
Daianu, M.[Madelaine],
Steeg, G.V.[Greg Ver],
Mezher, A.[Adam],
Jahanshad, N.[Neda],
Nir, T.M.[Talia M.],
Yan, X.[Xiaoran],
Prasad, G.[Gautam],
Lerman, K.[Kristina],
Galstyan, A.[Aram],
Thompson, P.M.[Paul M.],
Information-Theoretic Clustering of Neuroimaging Metrics Related to
Cognitive Decline in the Elderly,
MCV15(13-23).
Springer DOI
1608
BibRef
Ben Ahmed, O.[Olfa],
Benois-Pineau, J.[Jenny],
Ben Amar, C.[Chokri],
Aliara, M.[Michele],
Catheline, G.[Gwenaelle],
Features-based approach for Alzheimer's disease diagnosis using
visual pattern of water diffusion in tensor diffusion imaging,
ICIP15(2840-2844)
IEEE DOI
1512
AD-related signature
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Garg, S.[Saurabh],
Tang, L.[Lisa],
Traboulsee, A.[Anthony],
Tam, R.[Roger],
A sensitive and efficient method for measuring change in cortical
thickness using fuzzy correspondence in Alzheimer's disease,
ICIP15(3014-3018)
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:
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MLMI15(178-185).
Springer DOI
1511
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Amoroso, N.[Nicola],
Tangaro, S.[Sabina],
Errico, R.[Rosangela],
Garuccio, E.[Elena],
Monda, A.[Anna],
Sensi, F.[Francesco],
Tateo, A.[Andrea],
Bellotti, R.[Roberto], for the Alzheimer's Disease Neuroimaging Initiative,
An Hippocampal Segmentation Tool Within an Open Cloud Infrastructure,
ISCA15(193-200).
Springer DOI
1511
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Vanderweyen, D.[Davy],
Munsell, B.C.[Brent C.],
Mintzer, J.E.[Jacobo E.],
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Zhu, X.[Xun],
Wu, G.R.[Guo-Rong],
Joseph, J.[Jane], Alzheimers Disease Neuroimaging Initiative,
Identifying Abnormal Network Alterations Common to Traumatic Brain
Injury and Alzheimer's Disease Patients Using Functional Connectome
Data,
MLMI15(229-237).
Springer DOI
1511
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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
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Andersen, S.K.[Simon Kragh],
Jakobsen, C.E.[Christian Elmholt],
Pedersen, C.H.[Claus Hougaard],
Rasmussen, A.M.[Anders Munk],
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Classification of Alzheimer's Disease from MRI Using Sulcal Morphology,
SCIA15(103-113).
Springer DOI
1506
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain, Parkinson's Disease .