Professor, Deputy Director, Center for BIT
Shenzhen Institutes of Advanced Technology, CAS, China
Email: sq.wang@siat.ac.cn
Shuqiang Wang is currently a Professor with Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Science, China. He has pulished more than 100 SCI/EI papers on journals, such as IEEE TMI/TIP/TNNLS/TSMC/TSP//TII/TASE, Medical Image Analysis etc., and more than 30 patents; He served as an Associate Editor for Frontiers in Neuroscience, Brain Informatics, Big Data and Cloud Innovation; He was selected for "Top 2% Scientists Worldwide".
Research Areas
Machine learning, Brain image computing, Medical image computing, Big data
Publications
Selected Publications Since 2017
- 3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2023.3266819, 2023
Large-Scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement, IJCAI 2023
Fundus Image-label Pairs Synthesis and Retinopathy Screening via GANs with Class-imbalanced Semi-supervised Learning, IEEE Transactions on Medical Imaging, DOI:10.1109/TMI.2023.3263216, 2023
- Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain, IEEE Transactions on Neural Networks and Learning Systems, DOI:10.1109/TNNLS.2022.3153088, 2022
- Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2021.3118369, 2021
- Joint 3D trajectory and resource optimization in multi-UAV enabled IoT networks with wireless power transfer, IEEE Internet of Things Journal, 8(10):7833-7848, 2021
- Effective Distributed Learning with Random Features: Improved Bounds and Algorithms, ICLR, 2021
- Tensorizing GAN with High-Order Pooling for Alzheimer’s Disease Assessment, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2021.3063516, 2021
- Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis, IEEE Transactions on Medical Imaging, DOI: 10.1109/TMI.2021.3107013, 41(1): 145-157, 2022
- Predicting Clinical Scores for Alzheimer's Disease Based on Joint and Deep Learning, Expert Systems with Applications, 187:115966, 2022
- Insights Into Algorithms for Separable Nonlinear Least Squares Problems, IEEE Transactions on Image Processing, 30: 1207-1218, 2021
- Diabetic Retinopathy Diagnosis using Multi-channel Generative Adversarial Network with Semi-supervision, IEEE Transactions on Automation Science and Engineering,18(2):574-585, 2021
- An Ensemble based Densely-Connected Deep Learning System for Assessment of Skeletal Maturity, IEEE Transactions on Systems, Man and Cybernetics: Systems, 2022, 52(1):426-437
- Skin Lesion Segmentation via Generative Adversarial Networks with Dual Discriminators, Medical Image Analysis, 64: 10716, 2020
- Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network, MICCAI 2020, LNCS 12262:698-707, 2020
- Multi-Scale Enhanced Graph Convolutional Network for Early Mild Cognitive Impairment Detection, MICCAI 2020, LNCS 12267: 228–237, 2020.
- Deep and joint learning of longitudinal data for Alzheimer’s disease prediction, Pattern Recognition, 10: 107247, 2020
- Dominant-modes-based Sliding Mode Observer for Estimation of Temperature Distribution in Rapid Thermal Processing System, IEEE Transactions on Industrial Informatics, 15(5): 2673-2681, 2019
- Classification oF Diffusion tensor metrics for The Diagnosis of A Myelopathic Cord Using Machine Learning, International Journal of Neural Systems,6, 1750036, 2018
- Subcarrier Pairing Based Resource Optimization for OFDM Wireless Powered Relay Transmissions with Time Switching Scheme, IEEE Transactions on Signal Processing, 65(5): 1130-1145, 2017.
Students
现指导学生
陈卓 硕士研究生 085211-计算机技术
胡圣烨 硕士研究生 085210-控制工程
罗蔚然 硕士研究生 085208-电子与通信工程
奚桂锴 硕士研究生 085211-计算机技术
游森榕 硕士研究生 085211-计算机技术
胡博闻 硕士研究生 085211-计算机技术
杨戈 硕士研究生 085211-计算机技术
宫长威 硕士研究生 081200-计算机科学与技术
荆常宏 硕士研究生 085400-电子信息
王中昊 硕士研究生 085400-电子信息