Chong-Yaw Wee

发表

DICCCOL: dense individualized and common connectivity-based cortical landmarks.

D. Shen, Xi Jiang, Lei Guo, 2013, Cerebral cortex.

Selection of a Subset of EEG Channels using PCA to classify Alcoholics and Non-alcoholics

Chong-Yaw Wee, Kok-Meng Ong, K. Thung, 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

Prediction of Alzheimer's Disease and Mild Cognitive Impairment Using Cortical Morphological Patterns Chong-yaw Wee, Pew-thian Yap, and Dinggang Shen; for the Alzheimer's Disease Neuroimaging Initiative

D. Shen, P. Yap, Chong-Yaw Wee, 2022 .

Identification of infants at high‐risk for autism spectrum disorder using multiparameter multiscale white matter connectivity networks

D. Shen, F. Shi, P. Yap, 2015, Human brain mapping.

Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans

D. Shen, P. Yap, Chong-Yaw Wee, 2015, Brain Structure and Function.

High‐order resting‐state functional connectivity network for MCI classification

Gang Li, D. Shen, Han Zhang, 2016, Human brain mapping.

Image sharpness measure using eigenvalues

Chong-Yaw Wee, R. Paramesran, 2008, 2008 9th International Conference on Signal Processing.

Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification

Daoqiang Zhang, D. Shen, P. Yap, 2013, Brain Structure and Function.

Disrupted Brain Functional Network in Internet Addiction Disorder: A Resting-State Functional Magnetic Resonance Imaging Study

Dinggang Shen, Guorong Wu, Feng Shi, 2014, PloS one.

Multilevel Deficiency of White Matter Connectivity Networks in Alzheimer's Disease: A Diffusion MRI Study with DTI and HARDI Models

Dinggang Shen, Tao Wang, Feng Shi, 2016, Neural plasticity.

Maternal sensitivity predicts anterior hippocampal functional networks in early childhood

P. Gluckman, Chong-Yaw Wee, A. Qiu, 2019, Brain Structure and Function.

Altered Modular Organization of Structural Cortical Networks in Children with Autism

Dinggang Shen, Feng Shi, Chong-Yaw Wee, 2013, PloS one.

Neonatal neural networks predict children behavioral profiles later in life

Anqi Qiu, P. Gluckman, Chong-Yaw Wee, 2017, Human brain mapping.

Multi‐task diagnosis for autism spectrum disorders using multi‐modality features: A multi‐center study

Dinggang Shen, Qian Wang, Feng Zhao, 2017, Human brain mapping.

Classification of Rice Grain Using New Scale Invariant Zernike Moments

Raveendran Paramesran, Chong-Yaw Wee, Hiroshi Kadota, 2002, JCIS.

Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations

Anqi Qiu, Chong-Yaw Wee, Hui Ji, 2019, NeuroImage: Clinical.

Diagnosis of autism spectrum disorders using regional and interregional morphological features

Dinggang Shen, Feng Shi, Pew-Thian Yap, 2014, Human brain mapping.

Deep understanding of big multimedia data

Xiaofeng Zhu, Chong-Yaw Wee, Minjeong Kim, 2020, Neural Computing and Applications.

Fronto-parietal numerical networks in relation with early numeracy in young children

Chong-Yaw Wee, Qiang Wang, A. Qiu, 2018, Brain Structure and Function.

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D. Shen, F. Shi, Le An, 2015 .

Behavioral Heterogeneity in Relation with Brain Functional Networks in Young Children

P. Gluckman, Chong-Yaw Wee, S. Saw, 2018, Cerebral cortex.

Fronto-parietal numerical networks in relation with early numeracy in young children

Chong-Yaw Wee, A. Qiu, Y. Chong, 2018, Brain Structure and Function.

Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder

D. Shen, P. Yap, Xiaoying Tang, 2016, Brain Imaging and Behavior.

Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification

Daoqiang Zhang, D. Shen, P. Yap, 2013, Brain Structure and Function.

A brief review on multi-task learning

Kim-Han Thung, Chong-Yaw Wee, Chong-Yaw Wee, 2018, Multimedia Tools and Applications.

Image quality assessment by discrete orthogonal moments

Ramakrishnan Mukundan, Xudong Jiang, Raveendran Paramesran, 2010, Pattern Recognit..

Quantifying image distortions using SVD

Xudong Jiang, Raveendran Paramesran, Chong-Yaw Wee, 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

Quality Assessment of Gaussian Blurred Images Using Symmetric Geometric Moments

Raveendran Paramesran, Chong-Yaw Wee, R. Mukundan, 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

Enhancing the representation of functional connectivity networks by fusing multi‐view information for autism spectrum disorder diagnosis

Dinggang Shen, Chong-Yaw Wee, Seong-Whan Lee, 2018, Human brain mapping.

Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification

Dinggang Shen, Pew-Thian Yap, Chong-Yaw Wee, 2016, Brain Imaging and Behavior.

Machine Learning Techniques for AD/MCI Diagnosis and Prognosis

Daoqiang Zhang, Dinggang Shen, Luping Zhou, 2014, Machine Learning in Healthcare Informatics.

Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns

Dinggang Shen, Pew-Thian Yap, Chong-Yaw Wee, 2013, Human brain mapping.

Enriched white matter connectivity networks for accurate identification of MCI patients

Wenbin Li, Dinggang Shen, Pew-Thian Yap, 2011, NeuroImage.

MCI Identification by Joint Learning on Multiple MRI Data

Dinggang Shen, Yue Gao, Sven Haller, 2015, MICCAI.

Identification of MCI individuals using structural and functional connectivity networks

Daoqiang Zhang, Dinggang Shen, Pew-Thian Yap, 2012, NeuroImage.

Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion

Dinggang Shen, Kim-Han Thung, Pew-Thian Yap, 2014, NeuroImage.

Supervised Discriminative Group Sparse Representation for Mild Cognitive Impairment Diagnosis

Dinggang Shen, Heung-Il Suk, Chong-Yaw Wee, 2014, Neuroinformatics.

MRI-Based Intelligence Quotient (IQ) Estimation with Sparse Learning

Dinggang Shen, Xiaoying Tang, Heung-Il Suk, 2015, PloS one.

Multiple-Network Classification of Childhood Autism Using Functional Connectivity Dynamics

Dinggang Shen, Wei Gao, Chong-Yaw Wee, 2014, MICCAI.

Hyper-connectivity of functional networks for brain disease diagnosis

Daoqiang Zhang, Dinggang Shen, Chong-Yaw Wee, 2016, Medical Image Anal..

Identification of Infants at Risk for Autism Using Multi-parameter Hierarchical White Matter Connectomes

Dinggang Shen, Kim-Han Thung, Feng Shi, 2015, MLMI.

Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

Dinggang Shen, Yang Li, Chong-Yaw Wee, 2014, Neuroinformatics.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

Dinggang Shen, Heung-Il Suk, Chong-Yaw Wee, 2016, NeuroImage.

Adaptive Functional Connectivity Network Using Parallel Hierarchical BiLSTM for MCI Diagnosis

Jingyu Liu, Yang Li, Chong-Yaw Wee, 2019, MLMI@MICCAI.

Novel Effective Connectivity Inference Using Ultra-Group Constrained Orthogonal Forward Regression and Elastic Multilayer Perceptron Classifier for MCI Identification

Hao Yang, Yang Li, Chong-Yaw Wee, 2019, IEEE Transactions on Medical Imaging.

Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification

D. Shen, P. Yap, Chong-Yaw Wee, 2016, Brain Imaging and Behavior.

Diagnosis of Autism Spectrum Disorders Using Temporally Distinct Resting‐State Functional Connectivity Networks

Dinggang Shen, Pew-Thian Yap, Chong-Yaw Wee, 2016, CNS neuroscience & therapeutics.

Integrating Multiple Network Properties for MCI Identification

Daoqiang Zhang, Dinggang Shen, Heung-Il Suk, 2013, MLMI.

Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification

Daoqiang Zhang, Dinggang Shen, Chong-Yaw Wee, 2014, Human brain mapping.

Brain Disease Classification and Progression Using Machine Learning Techniques

Manhua Liu, Bo Cheng, Dinggang Shen, 2014 .

Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification

Dinggang Shen, Feng Liu, Chong-Yaw Wee, 2014, NeuroImage.

Integration of Network Topological and Connectivity Properties for Neuroimaging Classification

Daoqiang Zhang, Dinggang Shen, Wei Gao, 2014, IEEE Transactions on Biomedical Engineering.

Joint feature-sample selection and robust diagnosis of Parkinson's disease from MRI data

Ehsan Adeli, Guorong Wu, Feng Shi, 2016, NeuroImage.

A brief review on multi-task learning

Chong-Yaw Wee, Kim-Han Thung, 2018, Multimedia Tools and Applications.

Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification

Dinggang Shen, Yang Li, Jingyu Liu, 2019, Neuroinformatics.

Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients

Dinggang Shen, Pew-Thian Yap, Chong-Yaw Wee, 2012, PloS one.

Novel Effective Connectivity Network Inference for MCI Identification

Dinggang Shen, Ke Li, Hao Yang, 2017, MLMI@MICCAI.

Multimodal Hyper-connectivity Networks for MCI Classification

Dinggang Shen, Yang Li, Pew-Thian Yap, 2017, MICCAI.

Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification

Dinggang Shen, Ke Li, Yang Li, 2017, MLMI@MICCAI.

On the computational aspects of Zernike moments

Raveendran Paramesran, Chong-Yaw Wee, Chong-Yaw Wee, 2007, Image Vis. Comput..

Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data

Dinggang Shen, Martin Styner, Chong-Yaw Wee, 2015, NeuroImage.

Derivation of blur-invariant features using orthogonal Legendre moments

R. Paramesran, Chong-Yaw Wee, R. Paramesran, 2007 .

Classification of rice grains using fuzzy artmap neural network

Raveendran Paramesran, Chong-Yaw Wee, Hiroshi Kadota, 2002, Asia-Pacific Conference on Circuits and Systems.

Fusion of High-Order and Low-Order Effective Connectivity Networks for MCI Classification

Dinggang Shen, Ke Li, Yang Li, 2017, MLMI@MICCAI.

Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification

Dinggang Shen, Sen Yang, Pew-Thian Yap, 2013, Brain Imaging and Behavior.

Multimodal hyper‐connectivity of functional networks using functionally‐weighted LASSO for MCI classification

Dinggang Shen, Yang Li, Pew-Thian Yap, 2019, Medical Image Anal..

Discriminative Group Sparse Representation for Mild Cognitive Impairment Classification

Dinggang Shen, Heung-Il Suk, Chong-Yaw Wee, 2013, MLMI.

Constrained Sparse Functional Connectivity Networks for MCI Classification

Daoqiang Zhang, Dinggang Shen, Lihong Wang, 2012, MICCAI.

Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

D. Shen, Y. Li, Chong-Yaw Wee, 2014, Neuroinformatics.

Inter-modality Relationship Constrained Multi-Task Feature Selection for AD/MCI Classification

Dinggang Shen, Feng Liu, Chong-Yaw Wee, 2013, MICCAI.

Identification of Alzheimer's Disease Using Incomplete Multimodal Dataset via Matrix Shrinkage and Completion

Dinggang Shen, Kim-Han Thung, Pew-Thian Yap, 2013, MLMI.

High-Order Graph Matching Based Feature Selection for Alzheimer's Disease Identification

Dinggang Shen, Feng Liu, Heung-Il Suk, 2013, MICCAI.

Angular Resolution Enhancement of Diffusion MRI Data Using Inter-Subject Information Transfer.

Dinggang Shen, Yafeng Wu, Pew Thian Yap, 2016, Computational diffusion MRI : MICCAI Workshop, Munich, Germany, October 9th, 2015. CDMRI (Workshop).

Large Deformation Image Classification Using Generalized Locality-Constrained Linear Coding

Dinggang Shen, Marc Niethammer, Pew-Thian Yap, 2013, MICCAI.

Measure of image sharpness using eigenvalues

Raveendran Paramesran, Chong-Yaw Wee, Chong-Yaw Wee, 2007, Inf. Sci..

Groupwise Segmentation Improves Neuroimaging Classification Accuracy

Dinggang Shen, Yaping Wang, Lei Guo, 2012, MBIA.

Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification

D. Shen, Y. Li, P. Yap, 2019, Neuroinformatics.

Sorting of rice grains using Zernike moments

Raveendran Paramesran, Chong-Yaw Wee, Fumiaki Takeda, 2009, Journal of Real-Time Image Processing.

Fast Computation of Zernike Moments For Rice Sorting System

Raveendran Paramesran, Chong-Yaw Wee, Fumiaki Takeda, 2007, 2007 IEEE International Conference on Image Processing.

Efficient computation of radial moment functions using symmetrical property

Raveendran Paramesran, Chong-Yaw Wee, Chong-Yaw Wee, 2006, Pattern Recognit..

New computational methods for full and subset Zernike moments

Raveendran Paramesran, Chong-Yaw Wee, Fumiaki Takeda, 2004, Inf. Sci..

Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion

D. Shen, P. Yap, Chong-Yaw Wee, 2014, NeuroImage.

Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing

Ke Li, Pew-Thian Yap, Chong-Yaw Wee, 2016, Scientific Reports.

Structural Feature Selection for Connectivity Network-Based MCI Diagnosis

Daoqiang Zhang, Dinggang Shen, Chong-Yaw Wee, 2012, MBIA.

Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder

Dinggang Shen, Xiaoying Tang, Pew-Thian Yap, 2015, Brain Imaging and Behavior.

Feature reduction of Zernike moments using genetic algorithm for neural network classification of rice grain

Hiroshi Kadota, Paramesran Raveendran, F. Takeda, 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

Robust Group Comparison Using Non-Parametric Block-Based Statistics

Dinggang Shen, Sven Haller, Wenliang Pan, 2018, 1807.09157.