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College of Computer Science and Technology/College of Artificial Intelligence
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Office: 220, Building Computer Science and Technology |
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Short Biography
Daoqiang Zhang received the B.S. degree, and Ph.D. degree in Computer Science from
Nanjing University of Aeronautics and Astronautics (NUAA), China, in 1999, and 2004, respectively.
He joined the Department of Computer Science and Engineering of NUAA as a Lecturer in 2004, and is a professor at present.
His research interests include machine learning, pattern recognition, data mining, and medical image analysis. In these areas,
he has published over 200 scientific articles in refereed international journals such as IEEE Trans. Pattern Analysis and Machine Intelligence,
IEEE Trans. Medical Imaging, IEEE Trans. Image Processing, Neuroimage, Human Brain Mapping, Medical Image Analysis; and conference
proceedings such as IJCAI, AAAI, NIPS, CVPR, MICCAI, KDD, with 12,000+ citations by Google Scholar. He was nominated for the National
Excellent Doctoral Dissertation Award of China in 2006, won the best paper award or best student award of several international
conferences such as PRICAI'06, STMI'12 and BICS'16, etc. He has served as a program committee member for several international
conferences such as IJCAI, AAAI, NIPS, MICCAI, SDM, PRICAI, and ACML, etc. He is a member of the Machine Learning Society of the Chinese
Association of Artificial Intelligence (CAAI), and the Artificial Intelligence & Pattern Recognition Society of the China Computer Federation (CCF).
Research Interests
My
current research interests mainly include pattern
recognition, neural computing, machine learning, data mining and image
processing
Courses
Data Mining
For graduate students
Algorithm Design & Analysis For undergraduate students
Publications
[Journal Paper]
[2021]
l
Mingliang Wang, Jiangshuang Huang, Mingxia Liu, and Daoqiang Zhang.
Modeling dynamic characteristics of brain functional connectivity networks using resting-state functional MRI.
Medical Image Analysis, 71: 102063.
l
Wenyong Zhu, Liang Sun, Jiashuang Huang, Liangxiu Han, and Daoqiang Zhang.
Dual Attention Multi-Instance Deep Learning for Alzheimer's Disease Diagnosis with Structural MRI.
IEEE Transactions on Medical Imaging, 2021.
l
Mengting Xu, Tao Zhang, Zhongnian Li, Mingxia Liu, and Daoqiang Zhang.
Towards evaluating the robustness of deep diagnostic models by adversarial attack.
Medical Image Analysis, 69: 101977.
l
Xin Zhang, Liangxiu Han, Wenyong Zhu, Liang Sun, and Daoqiang Zhang.
An Explainable 3D Residual Self-Attention Deep Neural Network For Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI.
IEEE Journal of Biomedical and Health Informatics, 2021.
l
Meiling Wang, Wei Shao, Xiaoke Hao, and Daoqiang Zhang.
Identify Complex Imaging Genetic Patterns via Fusion Self-Expressive Network Analysis.
IEEE Transactions on Medical Imaging, 40(6): 1673-1686.
l
Peng Wan, Fang Chen, Chunrui Liu, Wentao Kong, and Daoqiang Zhang.
Hierarchical Temporal Attention Network for Thyroid Nodule Recognition Using Dynamic CEUS Imaging.
IEEE Transactions on Medical Imaging, 40(6): 1646-1660.
l
Shuo Huang, Wei Shao, Meiling Wang, and Daoqiang Zhang.
fMRI-based Decoding of Visual Information from Human Brain Activity: A Brief Review.
International Journal of Automation and Computing, 18: 170-184 (2021).
[2020]
l Wei Shao, Tongxin Wang, Liang Sun, Tianhan Dong, Zhi Han, Zhi Huang, Jie Zhang, Daoqiang Zhang, Kun Huang. Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers. Medical Image Analysis, 2020, 65: 101795.
l Jiashuang Huang, Mingliang Wang, Xijia Xu, Biao Jie, Daoqiang Zhang. A novel node-level structure embedding and alignment representation of structural networks for brain disease analysis. Medical Image Analysis, 2020, 65: 101755.
l Qi Zhu, Rui Zhang, Sheng-Jun Huang, Zheng Zhang, Daoqiang Zhang. LGSLRR: Towards fusing discriminative ordinal local and global structured low-rank representation for image recognition. Information Sciences, 2020, 539: 522-535.
l Xiaoke Hao, Yongjin Bao, Yingchun Guo, Ming Yu, Daoqiang Zhang, Shannon L. Risacher, Andrew J. Saykin, Xiaohui Yao, Li Shen, for the Alzheimer's Disease Neuroimaging Initiative. Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease. Medical Image Analysis, 2020, 60: 101625.
l Liang Sun, Wei Shao, Daoqiang Zhang, Mingxia Liu. Anatomical Attention Guided Deep Networks for ROI Segmentation of Brain MR Images. IEEE Transactions on Medical Imaging, 2020, 39(6): 2000-2012.
l Jiashuang Huang, Luping Zhou, Lei Wang, Daoqiang Zhang. Attention-diffusion-bilinear neural network for brain network analysis. IEEE Transactions on Medical Imaging, 2020, 39(7): 2541-2552.
l Liang Sun, Zhanhao Mo, Fuhua Yan, Liming Xia, Fei Shan, Zhongxiang Ding, Bin Song, Wanchun Gao, Wei Shao, Feng Shi, Huan Yuan, Huiting Jiang, Dijia Wu , Ying Wei, Yaozong Gao, He Sui, Daoqiang Zhang, and Dinggang Shen. Adaptive feature selection guided deep forest for COVID-19 classification with chest CT. IEEE Journal of Biomedical and Health Informatics, 2020, 24(10): 2798-2805.
l Muhammad Yousefnezhad, Alessandro Selvitella, Liangxiu Han, Daoqiang Zhang. Supervised Hyperalignment for multi-subject fMRI data alignment. IEEE Transactions on Cognitive and Developmental Systems, doi: 10.1109/TCDS.2020.2965981.
l
Kai Ma, Yongkang Liu, Wei Shao, Jianhua Sun, Jing Li, Xiaokun Fang, Zhongqiu Wang, and Daoqiang Zhang.
Brain Functional Interaction of Acupuncture Effects in Diarrhea-Dominant Irritable Bowel Syndrome.
Frontiers in Neuroscience, 14, 1261.
l
Qi Zhu, Nuoya Xu, Zheng Zhang, Donghai Guan, Ran Wang, and Daoqiang Zhang.
Cross-spectral palmprint recognition with low-rank canonical correlation analysis.
Multimedia Tools and Applications, 79(45): 33771-33792.
l
Peng Wan, Fang Chen, Wei Shao, Chunrui Liu, Yidan Zhang, Baojie Wen, Wentao Kong, and Daoqiang Zhang.
Irregular Respiratory Motion Compensation for Liver Contrast-enhanced Ultrasound via Transport based Motion Estimation.
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 68(4): 1117-1130.
l
Muhammad Yousefnezhad, Jeffrey Sawalha, Alessandro Selvitella, and Daoqiang Zhang.
Deep Representational Similarity Learning for Analyzing Neural Signatures in Task-based fMRI Dataset.
Neuroinformatics, 2020.
l Qi Zhu, Xiangyu Xu, Ning Yuan, Zheng Zhang, and Daoqiang Zhang. Latent correlation embedded discriminative multi-modal data fusion. Signal Processing, 2020, 171:107466.
l
[2019]
l
Mingliang
Wang, Daoqiang Zhang, Dinggang Shen,
Mingxia Liu. Multi-task
exclusive relationship learning for Alzheimer's disease progression prediction
with longitudinal data. Medical
Image Analysis, 2019, 53: 111-122.
l
Muhammad
Yousefnezhad, Daoqiang Zhang. Multi-Objective
Cognitive Model: a Supervised Approach for Multi-subject fMRI Analysis. Neuroinformatics,
2019, 17(2): 197-210.
l
Jiashuang
Huang, Qi Zhu, Xiaoke Hao, Xiaomeng Shi, Shuzhan
Gao, Xijia Xu, Daoqiang Zhang.
Identifying
Resting-State Multifrequency Biomarkers via Tree-Guided Group Sparse Learning
for Schizophrenia Classification. IEEE
Journal of Biomedical and Health Informatics,
2019 23(1): 342-350.
[2018]
l
Muhammad
Yousefnezhad, Daoqiang Zhang. Anatomical
Pattern Analysis for Decoding Visual Stimuli in Human Brains. Cognitive Computation,
2018, 10(2): 284-295.
l
Daoqiang Zhang, Jiashuang Huang, Biao Jie, Junqiang Du, Liyang Tu,
Mingxia Liu. Ordinal
Pattern: A New Descriptor for Brain Connectivity Networks. IEEE Transactions on Medical Imaging,
2018, 37(7): 1711-1722.
l
Biao Jie,
Mingxia Liu, Daoqiang Zhang, Dinggang
Shen. Sub-network
Kernels for Measuring Similarity of Brain Connectivity Networks in Disease
Diagnosis. IEEE Transactions on
Image Processing, 2018, 27(5): 2340-2353.
l
Bo Cheng,
Mingxia Liu, Daoqiang Zhang, Dinggang
Shen. Robust
Multi-Label Transfer Feature Learning for Early Diagnosis of Alzheimer’s
Disease. Brain Imaging and Behavior,
2018, 13:138–153.
l
Wei Shao,
Mingxia Liu, Ying-Ying Xu, Hong-Bin Shen, Daoqiang
Zhang. An
Organelle Correlation-Guided Feature Selection Approach for Classifying
Multi-Label Subcellular Bio-images. IEEE/ACM
Transactions on Computational Biology and Bioinformatics, 2018, 15(3):
828-838.
[2017]
l
Xiaoke Hao,
Chanxiu Li, Jingwen Yan, Xiaohui Yao, Shannon L. Risacher, Andrew J. Saykin, Li
Shen, Daoqiang Zhang. Identification
of associations between genotypes and longitudinal phenotypes via
temporally-constrained group sparse canonical correlation analysis. Bioinformatics, 2017, 33: i341-i349.
l
Muhammad
Yousefnezhad, Sheng-Jun Huang, Daoqiang Zhang.
WoCE:
a framework for clustering ensemble by exploiting the wisdom of Crowds theory.
IEEE Transactions on Cybernetics,
2017, 48(2): 486-499.
l
Wei Shao,
Yi Ding, Hongbin Shen, Daoqiang Zhang.
Deep
model-based feature extraction for predicting protein subcellular localizations
from bio-images. Frontiers of
Computer Science, 2017, 11(2): 243-252.
l
Chen Zu,
Linling Zhu, Daoqiang Zhang.
Iterative sparsity score for feature selection and its extension for multimodal
data. Neurocomputing, 2017, 259:
146-153.
l
Dan Zhang,
Qi Zhu, Daoqiang Zhang. Multi-modal
dimensionality reduction using effective distance. Neurocomputing, 2017, 259: 130-139.
l
Xiaoke Hao,
Chanxiu Li, Lei Du, Xiaohui Yao, Jingwen Yan, Shannon L. Risacher, Andrew J.
Saykin, Li Shen, Daoqiang Zhang. Mining Outcome-relevant Brain
Imaging Genetic Associations via Three-way Sparse Canonical Correlation
Analysis in Alzheimer’s Disease. Scientific
Reports, 2017, 7:44272.
l
Bo Cheng,
Mingxia Liu, Dinggang Shen, Zuoyong Li, Daoqiang
Zhang. Multi-Domain
Transfer Learning for Early Diagnosis of Alzheimer’s Disease. Neuroinformatics, 2017, 15:115-132.
l
Chen Zu,
Zhengxia Wang, Daoqiang Zhang, Peipeng Liang, Yonghong Shi, Dinggang Shen, Guorong Wu.
Robust
multi-atlas label propagation by deep sparse representation. Pattern Recognition, 2017, 63: 511-517.
[2016]
l
Biao Jie,
Mingxia Liu, Jun Liu, Daoqiang Zhang,
Dinggang Shen. Temporally
constrained group sparse learning for longitudinal data analysis in Alzheimer's
disease. IEEE Transactions on
Biomedical Engineering, 2016, 64(1): 238-249.
l
Mingxia
Liu, Daoqiang Zhang, Dinggang Shen. Relationship
Induced Multi-Template Learning for Diagnosis of Alzheimer’s Disease and
Mild Cognitive Impairment. IEEE
Transactions on Medical Imaging, 2016, 35(6): 1463-1474.
l
Biao Jie,
Chong-Yaw Wee, Dinggang Shen, Daoqiang Zhang.
Hyper-Connectivity
of Functional Networks for Brain Disease Diagnosis. Medical Image Analysis, 2016, 32: 84-100.
l
Tingting
Ye, Chen Zu, Biao Jie, Dinggang Shen, Daoqiang Zhang.
Discriminative
multi-task feature selection for multi-modality classification of
Alzheimer’s disease. Brain
Imaging and Behavior, 2016, 10: 1148-1159.
l
Chen Zu,
Biao Jie, Mingxia Liu, Songcan Chen, Dinggang Shen, Daoqiang Zhang. Label-aligned
multi-task feature learning for multimodal classification of Alzheimer’s
disease and mild cognitive impairment. Brain
Imaging and Behavior, 2016, 10: 739-749.
l
Xiaoke Hao,
Xiaohui Yao, Jingwen Yan, Shannon L. Risacher, Andrew J. Saykin, Daoqiang Zhang, Li Shen. Identifying
Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease
Status in Alzheimer’s Disease. Neuroinformatics,
2016, 14: 439-452.
l
Ying Liu,
Lan Tan, Hui-Fu Wang, Yong Liu, XiaoKe Hao, Chen-Chen Tan, Teng Jiang, Bing
Liu, Daoqiang Zhang, Jin-Tai Yu. Multiple
Effect of APOE Genotype on Clinical and Neuroimaging Biomarkers Across
Alzheimer’s Disease Spectrum. Molecular Neurobiology, 2016, 53:
4539-4547.
l
Hui-Fu
Wang, Yu Wan, Xiao-Ke Hao, Lei Cao, Xi-Chen Zhu, Teng Jiang, Meng-Shan Tan, Lin
Tan, Daoqiang Zhang, Lan Tan, Jin-Tai
Yu. Bridging
Integrator 1 (BIN1) Genotypes Mediate Alzheimer’s Disease Risk by
Altering Neuronal Degeneration. Journal
of Alzheimer's Disease, 2016, 52(1): 179-190.
l
Qing-Fei
Zhao, Yu Wan, Hui-Fu Wang, Fu-Rong Sun, Xiao-Ke Hao, Meng-Shan Tan, Chen-Chen
Tan, Daoqiang Zhang, Lan Tan, Jin-Tai
Yu. ABCA7
Genotypes Confer Alzheimer’s Disease Risk by Modulating Amyloid-β
Pathology. Journal of Alzheimer's
Disease, 2016,
52(2), 693-703.
l
Chong Wang,
Hui-Fu Wang, Meng-Shan Tan, Ying Liu, Teng Jiang, Daoqiang Zhang, Lan Tan, Jin-Tai Yu, Alzheimer’s
Disease Neuroimaging Initiative. Impact of Common Variations in PLD3 on
Neuroimaging Phenotypes in Non-demented Elders. Molecular Neurobiology, 2016, 53: 4343-4351.
l
Chen Zu, Daoqiang Zhang. Canonical
sparse cross-view correlation analysis. Neurocomputing, 2016, 191: 263-272.
l
Mingxia
Liu, Daoqiang Zhang. Feature
Selection with Effective Distance. Neurocomputing,
2016, 215: 100-109.
l
Yinghuan
Shi, Yaozong Gao, Shu Liao, Daoqiang Zhang, Yang Gao, Dinggang Shen.
A
learning-based CT prostate segmentation method via joint transductive feature
selection and regression. Neurocomputing,
2016, 173: 317-331.
l
Yanyan
Zhang, Jianchun Zhang, Zhisong Pan, Daoqiang Zhang.
Multi-view
dimensionality reduction via canonical random correlation analysis. Frontiers of Computer Science, 2016,
856-869.
l
Junqiang
Du, Lipeng Wang, Biao Jie, Daoqiang Zhang.
Network-based
classification of ADHD patients using discriminative subnetwork selection and
graph kernel PCA. Computerized
Medical Imaging and Graphics, 2016, 52: 82-88.
l
Muhammad
Yousefnezhad, Ali Reihanian, Daoqiang Zhang, Behrouz Minaei-Bidgoli.
A new selection strategy for selective cluster ensemble based on Diversity and Independency.
Engineering Applications of Artificial Intelligence, 2016, 56: 260-272.
[2015]
l
Mingxia
Liu, Daoqiang Zhang, Songcan Chen,
Hui Xue. Joint
Binary Classifier Learning for ECOC-based Multi-class Classification.
IEEE Transactions on Pattern Analysis
and Machine Intelligence, 2016, 38(11): 2335-2341.
l
Yinghua
Shi, Yaozhong Gao, Shu Liao, Daoqiang
Zhang, Yang Gao, Dinggang Shen. Semi-Automatic
Segmentation of Prostate in CT Images via Coupled Feature Representation and
Spatial-Constrained Transductive Lasso. IEEE Transactions on Pattern Analysis and Machine Intelligence,
2015, 37(11): 2286-2303.
l
Mingxia
Liu, Daoqiang Zhang. Pairwise
Constraint-Guided Sparse Learning for Feature Selection. IEEE Transactions on Cybernetics, 2015,
46(1): 298-310.
l
Bo Cheng,
Mingxia Liu, Daoqiang Zhang, Brent C.
Munsell, Dinggang Shen. Domain Transfer
Learning for MCI Coversion Prediction. IEEE
Transaction on Biomedical Engineering, 2015, 62(7), 1805-1817.
l
Mingxia
Liu, Daoqiang Zhang, Ehsan
Adeli-Mosabbeb, Dinggang Shen. Inherent
Structure Based Multi-view Learning with Multi-template Feature Representation
for Alzheimer’s Disease Diagnosis. IEEE Transactions on Biomedical Engineering, 2015, 63(7):
1473-1482.
l
Wei Shao,
Mingxia Liu, Daoqiang Zhang. Human
cell structure-driven model construction for predicting protein subcellular
location from biological images. Bioinformatics,
2015, 32(1): 114-121.
l
Dinggang
Shen, Daoqiang Zhang, Alastair Young,
Bahram Parvin. Editorial:
Machine Learning and Data Mining in Medical Imaging. IEEE Journal of Biomedical and Health Informatics, 2015, 19(5):
1587-1588.
l
Mingxia
Liu, Daoqiang Zhang, Dinggang Shen. View-centralized
multi-atlas classification for Alzheimer's disease diagnosis. Human Brain Mapping, 2015, 36(5): 1847-1865.
l
Bo Cheng,
Mingxia Liu, Heung-Il Suk, Dinggang Shen, Daoqiang
Zhang. Multimodal
manifold-regularized transfer learning for MCI conversion prediction. Brain Imaging and Behavior, 2015, 9(4):
913-926.
l
Biao Jie, Daoqiang Zhang, Bo Cheng, Dinggang Shen.
Manifold
regularized multitask feature learning for multimodality disease classification.
Human Brain Mapping, 2015, 36(2), 489-507.
l
Qi Zhu, Daoqiang Zhang, Han Sun, Zhengming Li. Combining
L1-norm and L2-norm based sparse representations for face recognition. Optik-International Journal for Light and
Electron Optics, 2015, 126(7): 719-724.
l
Huifu Wang,
Lan Tan, Xiaoke Hao, Teng Jiang, Mengshan Tan, Ying Liu, Daoqiang Zhang, Jintai Yu. Effect
of EPHA1 Genetic Variation on Cerebrospinal Fluid and Neuroimaging Biomarkers
in Healthy, Mild Cognitive Impairment and Alzheimer's Disease Cohorts. Journal of Alzheimer’s Disease,
2015, 44:115–123.
l
Chong Wang,
Lan Tan, Hui-Fu Wang, Wan-Jiang Yu, Ying Liu, Teng Jiang, Meng-Shan Tan,
Xiao-Ke Hao, Daoqiang Zhang, Jin-Tai
Yu. Common
variants in PLD3 and correlation to amyloid-related phenotypes in
Alzheimer’s disease. Journal
of Alzheimer's Disease, 2015, 46(2): 491-495.
[2014]
l
Chong-Yaw
Wee, Pew-Thian Yap, Daoqiang Zhang,
Lihong Wang, Dinggang Shen. Group-Constrained
Sparse FMRI Connectivity Modeling for Mild Cognitive Impairment Identification,
Brain Structure and Function, 2014,
219(2): 641-656.
l
Biao Jie,
and Daoqiang Zhang, Chong-Yaw Wee,
Dinggang Shen. Topological
Graph Kernel on Multiple Thresholded Functional Connectivity Networks for Mild Cognitive
Impairment Classification. Human
Brain Mapping, 2014, 35:2876–2897.
l
Manhua Liu,
Daoqiang Zhang, Dinggang Shen. Hierarchical
Fusion of Features and Classifier Decisions for Alzheimer's Disease Diagnosis.
Human Brain Mapping, 2014, 35(4):
1305- 319.
l
Guorong Wu,
Qian Wang, Daoqiang Zhang, Feiping
Nie, Heng Huang, Dinggang Shen. A
generative probability model of joint label fusion for multi-atlas based brain
segmentation. Medical Image Analysis,
2014, 18: 881–890.
l
Biao Jie, Daoqiang Zhang, Wei Gao, Qian Wang,
Chong-Yaw Wee, Dinggang Shen. Integration of
Network Topological and Connectivity Properties for Neuroimaging Classification.
IEEE Transaction Biomedical Engineering,
2014, 61(2): 576-589.
l
Mingxia
Liu, Linsong Miao, and Daoqiang Zhang.
Two-Stage
Cost-Sensitive Learning for Software Defect Prediction. IEEE Transaction Reliability, 2014,
63(2): 676-686.
l
Manhua Liu,
Daoqiang Zhang, Dinggang Shen. Identifying
Informative Imaging Biomarkers via Tree Structured Sparse Learning for AD
Diagnosis. Neuroinformatics,
2014, 12: 381–394.
l
Mingxia
Liu, and Daoqiang Zhang, Songcan
Chen. Attribute
Relation Learning for Zero-shot Classification. Neurocomputing, 2014, 139: 34–46.
l
Mingxia
Liu, and Daoqiang Zhang. Sparsity
Score: A novel graph-preserving feature selection method. International Journal of Pattern
Recognition and Artificial Intelligence, 2014, 28(4): 1450009-1-29.
l
Ying Liu,
Jintai Yu, Huifu Wang, Xiaoke Hao, Yufen Yang, Teng Jiang, Xichen Zhu, Lei Cao,
Daoqiang Zhang, Lan Tan. Association
between NME8 Locus Polymorphism and Cognitive Decline, Cerebrospinal Fluid
and Neuroimaging Biomarkers in Alzheimer’s Disease. PLoS ONE, 2014, 9(12): e114777.
l
Fei Fei,
Biao Jie, Daoqiang Zhang. Frequent and
Discriminative Subnetwork Mining for Mild Cognitive Impairment Classification,
Brain Connectivity, 2014, 4(5):
347-360.
[2013]
l
Bo Cheng,
and Daoqiang Zhang, Songcan Chen,
Daniel Kaufer, Dinggang Shen. Semi-Supervised
Multimodal Relevance Vector Regression Improves Cognitive Performance
Estimation from Imaging and Biological Biomarkers. Neuroinformatics, 2013, 11(3): 339-353.
l
Shan Gao,
Chen Zu, and Daoqiang Zhang. Learning
mid-perpendicular hyperplane similarity from cannot-link constraints. Neurocomputing, 2013, 113: 195-203.
l
Fengshan
Wang, Daoqiang Zhang. A New
Locality-Preserving Canonical Correlation Analysis Algorithm for Multi-View
Dimensionality Reduction. Neural
Processing Letters, 2013, 37(2): 135-146.
[2012]
l
Daoqiang Zhang, Dinggang Shen. Multi-Modal
Multi-Task Learning for Joint Prediction of Multiple Regression and
Classification Variables in Alzheimer's Disease. Neuroimage, 2012, 59(2): 895-907.
l
Chong-Yaw
Wee, Pew-Thian Yap, Daoqiang Zhang,
Kevin Denny, Jeffrey N. Browndyke, Guy G. Potter, Kathleen Welsh-Bohmer, Lihong
Wang, Dinggang Shen. Identification
of MCI Individuals Using Structural and Functional Connectivity Networks. NeuroImage, 2012, 59(3): 2045- 2056
l
Manhua Liu,
Daoqiang Zhang, Dinggang Shen. Ensemble
Sparse Classification of Alzheimer's Disease. 2012, Neuroimage,
60(2): 1106-1116.
l
Daoqiang Zhang, Dinggang Shen. Predicting
Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal
Biomarkers. PLoS ONE, 2012,
7(3): e33182.
[2011]
l Daoqiang
Zhang, Yaping Wang, Luping
Zhou, Hong Yuan, Dinggang Shen.
Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage, 2011, 55(3): 856-867.
l Shiguo Chen, Daoqiang
Zhang. Semi-supervised Dimensionality Reduction
with Pairwise Constraints for Hyperspectral Image Classification. IEEE Geoscience & Remote Sensing
Letters, 2011, 8(2): 369-373.
l Jianchun Zhang, Daoqiang Zhang. A novel ensemble
construction method for multi-view data using random cross-view correlation
between within-class examples. Pattern
Recognition, 2011, 44(6): 1162-1171.
[2010]
l Enliang Hu, Songcan Chen, Daoqiang Zhang, Xuesong Yin. Semisupervised
Kernel Matrix Learning by Kernel Propagation. IEEE Transactions on Neural Networks, 2010, 21(11): 1831-1841.
l Weiling Cai, Songcan Chen, Daoqiang Zhang. A multiobjective
simultaneous learning framework for clustering and classification. IEEE Transactions on Neural Networks,
2010, 21(2): 185-200.
l Yan Peng, Daoqiang
Zhang, Jianchun Zhang. A New Canonical
Correlation Analysis Algorithm with Local Discrimination. Neural Processing Letters, 2010, 31(1):
1-15.
l Pengfei Huang, Daoqiang Zhang. Locality sensitive
C-means clustering algorithms. Neurocomputing,
2010, 73(16-18): 2935-2943.
l Xuesong Yin, Songcan Chen, Enliang Hu, Daoqiang Zhang.
Semi-supervised clustering with metric learning: An adaptive kernel method. Pattern Recognition, 2010, 43(4):
1320-1333.
l Dan Sun, Daoqiang
Zhang. Bagging Constraint Score for feature
selection with pairwise constraints. Pattern
Recognition, 2010, 43(6): 2106-2118.
[2009]
l
Dan Sun, Daoqiang Zhang.
A
New Discriminant Principal Component Analysis Method with Partial Supervision. Neural
Processing Letters, 2009, 30(2): 103-112.
l
Fei Wang,
Xin Wang, Daoqiang Zhang, Changshui
Zhang, Tao Li.
marginFace: A novel face recognition method by
average neighborhood margin maximization. Pattern
Recognition, 2009, 42(11): 2863-2875.
l
Weiling
Cai, Songcan Chen and Daoqiang Zhang.
A Simultaneous Learning Framework for Clustering and Classification. Pattern
Recognition, 2009,
42(7): 1248-1259.
[2008]
l Zhe Wang, Songcan Chen, Jun
Liu and Daoqiang Zhang.
Pattern
representation in feature extraction and classification- matrix versus vector.
IEEE Transactions on Neural Networks,
2008, 19(5): 758-769.
l
style='mso-bidi-font-style:normal'>Daoqiang
Zhang, Songcan Chen, and
Zhi-Hua Zhou. Constraint score: A new filter method for
feature selection with pairwise constraints. Pattern Recognition, 2008, 41(5): 1440-1451.
[2007]
l Weiling Cai, Songcan Chen and Daoqiang Zhang. Fast
and Robust Fuzzy C-Means Clustering Algorithms Incorporating Local Information
for Image Segmentation. Pattern Recognition,
2007, 40(3):825-838. (Best Paper Award Honorable Mention)
l Daoqiang
Zhang, Songcan Chen and Zhi-Hua Zhou.
Entropy-inspired competitive clustering algorithms. International Journal of Software and
Informatics, 2007, 1(1):67-84.
l
Jun Liu,
Songcan Chen, Xiaoyang Tan and Daoqiang
Zhang. Efficient pseudo-inverse linear discriminant analysis and its
nonlinear form for face recognition. International
Journal of Pattern Recognition and Artificial Intelligence, 2007, 21(8):
1265-1278.
l Jun Liu, Songcan Chen, Xiaoyang Tan and Daoqiang Zhang. Comments
on "Efficient and Robust Maximal Margin Criterion". IEEE Transactions on Neural Networks,
2007, 18(6), 1862-1864.
l Weiling Cai, Songcan Chen and Daoqiang Zhang.
Robust fuzzy relational classifier incorporating the soft class labels.
Pattern Recognition Letters, 2007,
28(16): 2250-2263.
[2006]
l Daoqiang Zhang, Songcan Chen, and Zhi-Hua Zhou.
Learning the kernel parameters in kernel minimum distance classifier. Pattern
Recognition, 2006, 39(1):133-135.
l Daoqiang Zhang, Zhi-Hua Zhou and Songcan. Chen.
Diagonal principal component analysis for face recognition.
Pattern Recognition, 2006,
39(1):140-142.
[2005]
l Daoqiang Zhang and Zhi-Hua Zhou.
(2D)2PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition.
Neurocomputing, 2005, 69: 224-231.
l Daoqiang Zhang, Songcan Chen.
Fast image compression using matrix K-L transform, Neurocomputing, 2005, 68: 258-266.
l Songcan Chen, Yulian Zhu, Daoqiang Zhang, Jing-Yu Yang.
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Pattern Recognition Letters, 2005,
26(8): 1157-1167.
l Daoqiang
Zhang, Songcan Chen, Zhi-Hua
Zhou.
A new face recognition method based on SVD perturbation for single example image per person. Applied Mathematics and Computation, 2005, 163(2): 895-907.
l Daoqiang Zhang, Songcan Chen, Keren Tan.
Improving the robustness of 'online agglomerative clustering method' based on kernel-induce distance measures.
Neural Processing Letters, 2005, 21(1): 45-51.
[2004]
l Daoqiang Zhang, Songcan Chen.
A novel kernelised fuzzy c-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine,
2004, 32(1):37-50.
l Songcan Chen, Daoqiang Zhang.
Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance metric.
IEEE Trans. on System, Man and Cybernetics-Part B, 2004, 34(4): 1907-1916.
l Songcan Chen, Daoqiang Zhang, Zhi-Hua Zhou.
Enhanced (PC)2A for face recognition with one training image per person.
Pattern Recognition Letters, 2004, 25(10): 1173-1181.
l Daoqiang Zhang, Songcan Chen.
A comment on 'Alternative c- means clustering algorithms'.
Pattern Recognition, 2004, 37(2): 173-174.
[2003]
l Daoqiang Zhang, Songcan Chen.
Clustering incomplete data using kernel-based fuzzy c-means algorithm. Neural Processing Letters, 2003, 18(3): 155-162.
l Daoqiang
Zhang, Songcan Chen.
A novel multi-valued BAM model with improved error-correcting capability.
Journal of Electronics, 2003, 20(3): 220-223.
[Conference Paper]
[2021]
l
Meiling Wang, Wei Shao, Shuo Huang, and Daoqiang Zhang.
Deep Self-Reconstruction Sparse Canonical Correlation Analysis For Brain Imaging Genetics.
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 1790-1793.
l
Xiaoxin Wang, Xuyun Wen, Kai Ma, and Daoqiang Zhang.
A Multilayer Maximum Spanning Tree Kernel For Brain Networks.
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 1582-1585.
l
Zhongnian Li, Tao Zhang, Wei Shao, Songcan Chen, and Daoqiang Zhang.
Sign-aware Perturbations Regression.
Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), 396-404.
[2020]
l
Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew J Greenshaw, and Russell Greiner.
Shared Space Transfer Learning for analyzing multi-site fMRI data.
The 24th Conference on Neural Information Processing Systems (NeurIPS'20).
l
Shuo Huang, Liang Sun, Muhammad Yousefnezhad, Meiling Wang, and Daoqiang Zhang.
Perceived Image Reconstruction from Human Brain Activity via Time-Series Information Guided Generative Adversarial Networks.
International Conference on Neural Information Processing (ICONIP 2020), 156-163.
l
Peng Wan and Daoqiang Zhang.
Depth-Adaptive Discriminant Projection with Optimal Transport.
Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2020), 164-174.
l
Jiashuang Huang, Xu Li, Mingliang Wang, and Daoqiang Zhang.
Hierarchical Representation Learning of Dynamic Brain Networks for Schizophrenia Diagnosis.
Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2020), 470-479.
[2019]
l
Mingliang
Wang, Jiashuang Huang, Mingxia Liu, Daoqiang
Zhang. Functional Connectivity Network Analysis with Discriminative Hub
Detection for Brain Disease Identification. AAAI 2019.
l
Zhongnian
Li, Tao Zhang, Daoqiang Zhang. SEGAN:
Structure-Enhanced Generative
Adversarial Network for Compressed Sensing MRI Reconstruction. AAAI 2019.
[2018]
l
Wei
Shao, Liang Sun, Daoqiang Zhang.
Deep active
learning for nucleus classification in pathology images. ISBI 2018: 199-202.
l
Chen
Zu, Yan Wang, Luping Zhou, Lei Wang, Daoqiang Zhang. Multi-modality
feature selection with adaptive similarity learning for classification of
Alzheimer's disease. ISBI 2018: 1542-1545.
l
Yi
Ding, Weiqing Liu, Jiang Bian, Daoqiang Zhang, Tie-Yan Liu. Investor-Imitator: A
Framework for Trading Knowledge Extraction. KDD 2018: 1310-1319.
l
Mingliang
Wang, Daoqiang Zhang, Jiashuang
Huang, Dinggang Shen, Mingxia Liu. Low-Rank
Representation for Multi-center Autism Spectrum Disorder Identification. MICCAI 2018: 647-654.
l
Wei
Shao, Jun Cheng, Liang Sun, Zhi Han, Qianjin Feng, Daoqiang Zhang, Kun Huang. Ordinal
Multi-modal Feature Selection for Survival Analysis of Early-Stage Renal Cancer. MICCAI 2018: 648-656.
[2017]
l
Muhammad
Yousefnezhad, Daoqiang Zhang. Deep Hyperalignment.
In: 31st Conference on Neural Information Processing Systems (NIPS’17), Long Beach, CA, 2017.
l
Muhammad
Yousefnezhad, Daoqiang Zhang. Local
Discriminant Hyperalignment for multi-subject fMRI data alignment. In: 2017
AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.
l
Yi Ding,
Shengjun Huang, Daoqiang Zhang. Margin
Distribution Logistic Machine. In: 2017 SIAM International Conference on
Data Mining (SDM'17), Houston,
Texas, 2017.
l
Muhammad
Yousefnezhad, Daoqiang Zhang. Multi-Region
Neural Representation. In: 2017 SIAM International Conference on Data
Mining (SDM'17), Houston, Texas,
2017.
l
Mingliang
Wang, Xiaoke Hao, Jiashuang Huang, Kangcheng Wang, Xijia Xu, Daoqiang Zhang. Multi-level
Multi-task Structured Sparse Learning for Diagnosis of Schizophrenia Disease.
In: International Conference on Medical Image Computing and Computer-Assisted
Intervention (MICCAI’17),
Quebec City, Canada, 2017.
l
Liang Sun,
Wei Shao, Daoqiang Zhang. High-order
Boltzmann machine-based unsupervised feature learning for multi-atlas
segmentation. In: IEEE International Symposium on Biomedical Imaging (ISBI’17), Melbourne, Australia,
2017.
[2016]
l
Mingxia
Liu, Junqiang Du, Biao Jie, Daoqiang
Zhang. Ordinal
Patterns for Connectivity Networks in Brain Disease Diagnosis. In:
International Conference on Medical Image Computing and Computer-Assisted
Intervention (MICCAI’16), Athens, Greece, 2016.
l
Xiaoke Hao,
Xiaohui Yao, Jingwen Yan, Shannon L, Risacher, Andrew J, Saykin, Daoqiang Zhang, Li Shen. Identifying
Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease
Status in Alzheimer’s Disease. In: Pacific Symposium on Biocomputing
(PSB’16), Hawaii, USA, 2016.
[2015]
l
Muhammad
Yousefnezhad, Daoqiang Zhang. Weighted Spectral
Cluster Ensemble. In: Proceedings of the IEEE International Conference on
Data Mining (ICDM'15), Atlantic
City, USA.
l
Mingxia
Liu, Daoqiang Zhang, Dinggang Shen. Inherent
Structure-Guided Multi-view Learning for Alzheimer’s Disease and Mild
Cognitive Impairment Classification. MICCAI Workshop on Machine Learning in
Medical Imaging (MLMI), Munich,
Germany. October 5, 2015.
l
Bo Cheng,
Mingxia Liu, Daoqiang Zhang. Multimodal
Multi-label Transfer Learning for Early Diagnosis of Alzheimer’s Disease.
MICCAI Workshop on Machine Learning in Medical Imaging (MLMI), Munich, Germany. October 5, 2015
[2014]
l
Xiaoke Hao,
Jintai Yu, Daoqiang Zhang. Identifying
Genetic Associations with MRI-derived Measures via Tree-Guided Sparse Learning.
In: International Conference on Medical Image Computing and Computer Assisted
Intervention (MICCAI), Boston, USA,
2014.
l
Biao Jie,
Dinggang Shen, Daoqiang Zhang. Brain
Connectivity Hyper-Network for MCI Classification. In: International
Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Boston, USA, 2014.
[2013]
l
Yinghuan
Shi, Shu Liao, Yaozong Gao, Daoqiang Zhang,
Yang Gao, Dinggang Shen. Prostate
Segmentation in CT Images via Spatial-Constrained Transductive Lasso. In:
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, USA, 2013.
l
Biao Jie, Daoqiang Zhang, Bo Cheng, Dinggang Shen.
Manifold
Regularized Multi-Task Feature Selection for Multi-Modality Classification in
Alzheimer's Disease. In: International Conference on Medical Image
Computing and Computer Assisted Intervention (MICCAI), Nagoya, Japan, 2013.
l
Guorong Wu,
Qian Wang, Shu Liao, Daoqiang Zhang,
Feiping Nie, Dinggang Shen. Minimizing
joint risk of mislabeling for iterative patch-based label fusion. In:
International Conference on Medical Image Computing and Computer Assisted
Intervention (MICCAI), Nagoya,
Japan, 2013.
[2012]
l
Manhua Liu,
Daoqiang Zhang, Pew-Thian Yap,
Dinggang Shen. Tree-Guided
Sparse Coding for Brain Disease Classification. In: International
Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France, Oct. 1-5, 2012.
l
Chong-Yaw
Wee, Pew-Thian Yap, Daoqiang Zhang,
Lihong Wang, Dinggang Shen. Constrained
Sparse Functional Connectivity Networks for MCI Classification. In:
International Conference on Medical Image Computing and Computer Assisted
Intervention (MICCAI), Nice, France,
Oct. 1-5, 2012.
l
Daoqiang Zhang, Jun Liu, Dinggang Shen.
Temporally-Constrained
Group Sparse Learning for Longitudinal Data Analysis. In: International
Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France, Oct. 1-5, 2012.
l
Bo Cheng, Daoqiang Zhang, Dinggang Shen. Domain Transfer
Learning for MCI Conversion Prediction. In: International Conference on
Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France, 2012.
[2011]
l Daoqiang Zhang, Dinggang Shen. Semi-supervised multimodal classification of Alzheimer's disease. The 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2011: 1628-1631.
l Daoqiang Zhang, Dinggang Shen. Multi-modal multi-task learning for joint prediction of clinical scores in alzheimer's disease. International Workshop on Multimodal Brain Image Analysis. Heidelberg Berlin: Springer, 2011: 60-67.
l Daoqiang Zhang, Guorong Wu, Hongjun Jia, Dinggang Shen. Confidence-guided sequential label fusion for multi-atlas based segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Heidelberg Berlin: Springer, 2011: 643-650.
[2010]
l Jianchun Zhang, Daoqiang Zhang. Canonical random
correlation analysis. In:
Proceedings of the 25th Symposium On Applied Computing (SAC’10), poster, Switzerland,
2010, 1111-1112.
[2009]
l Daoqiang
Zhang and Wanquan Liu.
An Efficient Nonnegative Matrix Factorization Approach in Flexible Kernel Space. In: Proceedings
of the 21st International Joint Conference on Artificial Intelligence
(IJCAI'09), Pasadena, CA, 2009.
[2008]
l Daoqiang
Zhang, Songcan Chen, Zhi-Hua
Zhou and Qiang Yang. Constraint projections for
ensemble learning. In: Proceedings
of the 23rd AAAI Conference on Artificial Intelligence (AAAI'08), Chicago,
IL, 2008.
[2007]
l Daoqiang
Zhang, Zhi-Hua Zhou, and
Songcan Chen.
Semi-supervised dimensionality reduction. In: Proceedings of the 7th SIAM
International Conference on Data Mining (SDM'07), Minneapolis, MN, 2007,
pp. 629-634.
l Daoqiang
Zhang.
Two-Dimensional Bayesian Subspace Analysis for Face Recognition. In: Proceedings of the 4th International Symposium on Neural Networks
(ISNN'07), Nanjing, China, LNCS 4492: 778-784, 2007.
l Weiling Cai, Songcan Chen and Daoqiang Zhang,
Enhanced Fuzzy Relational Classifier with Representative Training Samples, International Conference on Wavelet
Analysis and Pattern Recognition (ICWAPR’07),
112-117, 2007.
[2006]
l Daoqiang Zhang, Zhi-Hua Zhou and Songcan Chen.
Adaptive kernel principal component analysis with
unsupervised learning of kernels. In: Proceedings of the 6th IEEE International Conference on Data Mining
(ICDM'06), Hong Kong, China, 2006, pp.1178-1182.
l Daoqiang Zhang, Songcan Chen and Zhi-Hua Zhou. Recognizing face or object from a single
image: Linear vs. kernel methods on 2D patterns. In: Proceedings of the Joint IAPR International Workshops on Structural and
Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition
(S+SSPR'06), in conjunction with ICPR'06, Hong Kong, China, LNCS, 2006.
l Daoqiang Zhang, Zhi-Hua Zhou and Songcan Chen.
Non-negative matrix factorization on kernels. In:
Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06), Guilin, China, LNAI
4099, 2006. (Best Paper Award)
l Jun Liu, Songcan Chen, Xiaoyang Tan and Daoqiang Zhang. An
Efficient Pseudoinverse Linear Discriminant Analysis method for Face
Recognition. In: Proceedings of the
International Conferences on Neural Information Processing (ICONIP’06),
Hong Kong, China, LNCS, 2006.
[2005]
l Daoqiang Zhang, Songcan. Chen, and Zhi-Hua Zhou.
Two-dimensional non-negative matrix factorization for face representation and recognition.
In: Proceedings of the ICCV'05 Workshop on Analysis and Modeling of Faces and Gestures
(AMFG'05), Beijing, China, LNCS 3723:350-363, 2005.
l Daoqiang Zhang, Songcan Chen, Jun Liu.
Representing image matrices: Eigenimages vs. Eigenvectors.
In: Proceedings of the 2st International
Symposium on Neural Networks (ISNN'05), Chongqing, China, LNCS 3497: 659-664, 2005.
[2004]
l Keren Tan, Songcan Chen, Daoqiang Zhang.
Robust image denoising using kernel-induced measures. In:
Proceedings of the 17th International Conference on Pattern Recognition
(ICPR’04), Cambridge, UK, 2004, 4: 685-688.
l Daoqiang Zhang, Keren Tan, and Songcan Chen.
Semi-supervised kernel-based fuzzy c-means. In: Proceedings of the International
Conferences on Neural Information Processing (ICONIP’04), India, LNCS
3316: 1229-1234, 2004.
l Daoqiang
Zhang, Songcan Chen, Zhi-Hua.
Zhou.
Fuzzy-kernel learning vector quantization. In: Proceedings of the 1st International
Symposium on Neural Networks (ISNN'04), Dalian, China, LNCS 3173: 180-185, 2004.
[2003]
l Daoqiang
Zhang, Songcan Chen.
Kernel-based fuzzy and possibilistic c-means clustering.
In: Proceedings of the International
Conference on Artificial Neural Networks (ICANN’03), Istanbul,
Turkey, 2003, 122-125.
l Daoqiang Zhang, Songcan Chen, Zhisong Pan, Keren Tan.
Kernel-based fuzzy clustering incorporating spatial constraints for image segmentation.
In: Proceedings of the Second International Conference on Machine Learning and Cybernetics (ICMLC’03), Xi'an,
China, 2003, 2189-2192.
l Zhisong Pan, Songcan Chen, Genbao Hu, Daoqiang Zhang.
Hybrid neural network and C4.5 for misuse detection. In: Proceedings of the Second International Conference on Machine
Learning and Cybernetics (ICMLC’03), Xi'an, China, 2003, 2463-2467.
[2003]
l Daoqiang
Zhang, Songcan Chen.
Fuzzy clustering using kernel method. In: Proceedings of the 2002 International
Conference on Control and Automation (ICCA’02), Xiamen, China, 2002, 123-127.