Bin Gao, Aiju Yu, Chen Qiao*, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson and Yuping Wang. An Explainable Unified Framework of Spatio-Temporal Coupling Learning with Application to Dynamic Brain Functional Connectivity Analysis, IEEE Trans. Medical imaging, DOI: 10.1109/TMI.2024.3467384
Yingying Wang, Chen Qiao∗, Gang Qu, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson and Yu-Ping Wang, A Deep Dynamic Causal Learning Model to Study Changes in Dynamic Effective Connectivity during Brain Development, IEEE Trans. Biomedical Engineering, 2024, 71(12), 3390-3401
Longyun Chen, Yuhui Yang, Aiju Yu, Shuo Guo, Kai Ren, Qinfang Liu, Chen Qiao∗, An explainable spatio-temporal graph convolutional network for the biomarkers identification of ADHD, Biomedical Signal Processing and Control, 2025, 99, 106913
Longyun Chen, Chen Qiao∗, Kai Ren, Gang Qu, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yu-Ping Wang, Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development, NeuroImage, 2024, 298, 120771
Chen Qiao, Bin Gao, Yuechen Liu, Xinyu Hu, Wenxing Hu, Vince D. Calhoun, Yu-Ping Wang, Deep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivity, Medical Image Analysis, 2023, 90, 102941
Lan Yang, Chen Qiao∗, Huiyu Zhou, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yuping Wang. Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences from Three fMRI Paradigms, IEEE Transactions on Biomedical Engineering, 2023, 70(8), 2404-2415
Faming Xu, Chen Qiao∗, Huiyu Zhou, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yuping Wang. An explainable autoencoder with multi-paradigm fMRI fusion for identifying differences in dynamic functional connectivity during brain development, Neural Networks, 2023, 159, 185-197
Ruolin Gong, SiJie He, Tiantian Tian, Jian Chen, Yuewen Hao, Chen Qiao∗. FRCNN-AA-CIF: An automatic detection model of colon polyps based on attention awareness and context information fusion, Computers in Biology and Medicine, 2023, 158, 106787
Jiajia Li, Faming Xu, Na Gao, Yuanqiang Zhu, Yuewen Hao, Chen Qiao∗. Sparse non-convex regularization based explainable DBN in the analysis of brain abnormalities in schizophrenia, Computers in Biology and Medicine, 2023, 155, 106664
Chen Qiao, Lan Yang, Yan Shi, Hanfeng Fang and Yanmei Kang. Deep belief networks with self-adaptive sparsity. Applied Intelligence, 2022, 52, 237-253
Weizheng Yan, Gang Qu, Wenxing Hu, Anees Abrol, Biao Cai, Chen Qiao, Sergey M. Plis, Yu-Ping Wang, Jing Sui and Vince D. Calhoun. Deep learning in neuroimaging: promises and challenges. IEEE Signal Processing Magazine, 2022, 39(2): 87-98
Qi Huang, Chen Qiao*, Kaili Jing, Xu Zhu, Kai Ren. Biomarkers identification for Schizophrenia via VAE and GSDAE-based data augmentation. Computers in Biology and Medicine, 2022, 146, 105603
Aiju Yu, Longyun Chen, Chen Qiao∗. Graph Convolutional Network with Attention Mechanism for Discovering the Brain's Abnormal Activity of Attention Deficit Hyperactivity Disorder, 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2022, 1-5, doi: 10.1109/CISP-BMEI56279.2022.9979902
Chen Qiao, Xin-Yu Hu, Li Xiao, Vince D. Calhoun and Yu-Ping Wang. A deep autoencoder with sparse and graph Laplacian regularization for characterizing dynamic functional connectivity during brain development, Neurocomputing, 2021, 456, 97-108
Chen Qiao, Lan Yang, Vince D. Calhoun, Zong-Ben Xu and Yu-Ping Wang. Sparse deep dictionary learning identifies differences of time-varying functional connectivity in brain neuro-developmental study, Neural Networks, 2021, 135, 91-104
Xuewu Zhang, Yansheng Gong, Chen Qiao and Wenfeng Jing. Multiview deep learning based on tensor decomposition and its application in fault detection of overhead contact systems. The Visual Computer, 2021,
https://doi.org/10.1007/s00371-021-02080-y
Chen Qiao, Yan Shi, Yu-Xian Diao, Vince D. Calhoun and Yu-Ping Wang. Log-sum enhanced sparse deep neural network. Neurocomputing, 2020, 407(24), 206-220
Chen Qiao, Bin Gao, Yan Shi. SRS-DNN: a deep neural network with strengthening response sparsity. Neural Computing and Applications, 2020, 32(12), 8127-8142
Lan Yang, Shun Qi, Chen Qiao*, Yanmei Kang. Exploring the abnormal brain regions and abnormal functional connections in SZ by multiple hypothesis testing techniques. CMES-Computer Modeling in Engineering & Sciences, 2020, 125(1), 215-237
Na Gao, Chen Qiao*, Shun Qi, Kai Ren, Jian Chen and Han-Feng Fang. Biomarkers selection of abnormal functional connections in Schizophrenia with l2,1-2-norm based sparse regularization feature selection method. In Huang Ds., Jo KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. LNCS 12464, Springer, 145-158, 2020
Chen Qiao, Jiao Wu and Jian Chen. Application of deep learning in medical image analysis. Shanghai Medical & Pharmaceutical Journal, 2020, 41(23), 14-19. (In Chinese)
Chen Qiao, Bin Gao, Lu-Jia Lu, Vince D. Calhoun and Yu-Ping Wang. Two-Step Feature Selection for Identifying Developmental Differences in Resting fMRI Intrinsic Connectivity Networks. Applied Sciences-Basel, 2019, 9, 4298
Chen Qiao, Lujia Lu, Lan Yang and Paul J. Kennedy. Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology. Applied Sciences-Basel, 2019, 9, 2148
Chen Qiao, Bao Guo. On the Flexible Dynamics Analysis for the Unified Discrete-Time RNNs, Neural Processing Letters, 2019, 50(11): 1755-1771
Chen Qiao, Ke-Feng Sun and Bin Li. A Deep-layer Feature Selection Method Based on Deep Neural Networks, LNCS 10942, Springer, 542-551, 2018
Chen Qiao, Kefeng Sun, Wenfeng Jing and Yan Shi. Critical dynamical analysis for α-UAM RNNs without diagonal nonlinear requirements, Journal of Intelligent & Fuzzy Systems, 2017, 33: 1677-1685
Chen Qiao, Yan Shi, Bin Li and Tai An. A Novel Diagnosis Method for SZ by Deep Neural Networks, Lecture notes in computer science (LNCS 10387), Springer, 433-441, 2017
Chen Qiao, Wenfeng Jing and Yuping Wang. The General Critical Analysis for Continuous-time UPPAM Recurrent Neural Networks. NEUROCOMPUTING, 2016, 175 (Part A): 40-46
Chen Qiao, Dong Liang and Kefeng Sun. Dynamics Analysis for Generic Projection Continuous-time RNNs with Bounded Matrices. JOURNAL OF SYSTEMS SCIENCE AND COMPLEXITY, 2015, 28 (4): 799-812
Chen Qiao, Haibao Chen, Wenfeng Jing and Kefeng Sun. Towards establishing a meaningful and practical dynamics results for the unified RNN model. NEUROCOMPUTING, 2015, 157 (1): 315-322
Dong Liang, Chen Qiao and Zongben Xu. Enhancing Both Efficiency and Representational Capability of Isomap by Extensive Landmark Selection. MATHEMATICAL PROBLEMS IN ENGINEERING, 2015, Article ID 241436
Chen Qiao, Dongdong Lin, Shaolong Cao and Yuping Wang. The Efficient Diagnosis of Schizophrenia by Using Multi-Layer RBMs Deep Networks. 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 603-606, Washington D.C., USA
Li ChunZhong，Zongben Xu, Chen Qiao and Tao Luo. Hierarchical clustering driven by cognitive features. SCIENCE CHINA INFORMATION SCIENCES, 2014, 57(1): 1-14
Huizhong Mao, Chen Qiao, Wenfeng Jing, Xi Chen and Jinqin Mao, Convergence theory for unified discrete-time RNNs with quasi-symmetric connection, APPLIED MECHANICS AND MATERIALS (Corresponding Author), 2014, 538: 167-170
Chen Qiao, Wenfeng Jing and Zongben Xu. The UPPAM continuous-time RNN model and its critical dynamics. NEUROCOMPUTING, 2013, 106: 158-166
Xi Chen, Huizhong Mao and Chen Qiao. Without diagonal nonlinear requirements: The more general P-critical dynamical analysis for UPPAM recurrent neural networks. MATHEMATICAL PROBLEMS IN ENGINEERING (Corresponding Author), 2013, Article ID 760293 http://dx.doi.org/10.1155/2013/760293
Chen Qiao，Rui Zhang, Jing Yao, Xiangliang Kong, Changsheng Zhou.The research on the GC property for RNNs with limited matrix 2-norm，2013 4th Global Congress on Intelligent Systems (GCIS 2013) Hong Kong, China，82-88
Chen Qiao and Zongben Xu. Critical dynamics study on recurrent neural networks: Globally exponential stability. NEUROCOMPUTING, 2012, 77 (1): 205-211
Chen Qiao. Feasible Convergence analysis for RNNs with linear saturation operator under the negative definition condition. ICEICE 2012, 941-944
Wenfeng Jing, Deyu Meng, Chen Qiao and Zhiming Peng. Eliminating Vertical Stripe Defects on Silicon Steel Surface by L1/2 Regularization. MATHEMATICAL PROBLEMS IN ENGINEERING (Corresponding Author), 2011, Article ID 854674
Chen Qiao and Zongben Xu. On the P-critical dynamics analysis of projection recurrent neural networks. NEUROCOMPUTING, 2010, 73 (13-15): 2783-2788
Chen Qiao and Zongben Xu. A critical global convergence analysis of recurrent neural networks with general projection mappings. NEUROCOMPUTING, 2009, 72 (7-9): 1878-1886
Zongben Xu and Chen Qiao. Towards a unified feedback neural network theory: The uniformly pseudo-projection-anti-monotone net. ACTA MATHEMATICA SINICA-ENGLISH SERIES, 2011, 27 (2): 377-396
Chen Qiao and Zongben Xu. New critical analysis on global convergence of recurrent neural networks with projection mappings. ADVANCES IN NEURAL NETWORKS, 2007: 131-139
Chen Qiao and Zongben Xu. Global stability analysis of static recurrent neural networks with sigmoidal functions: a unified result. DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS (SERIES A), 2007, 14: 31-35
Chen Qiao and Zongben Xu. The Critically Exponential Stability for Static Continuous Recurrent Neural Networks with Sigmoidal Functions. ACTA MATHEMATICAE APPLICATAE SINICA (In Chinese), 2012，35(6)：961-971
Li ChunZhong，Zongben Xu and Chen Qiao. Hierarchical agglomerative clustering with information feedback. SCIENCE CHINA, F: INFORMATION SCIENCES (In Chinese), 2012，42 (6): 730-742
Chen Qiao and Zongben Xu. Analysis on Global Convergence for Local Field Recurrent Neural Networks. ACTA MATHEMATICAE APPLICATAE SINICA (In Chinese), 2009, 32 (3): 536-545
