School of biomedical engineering, Shanghai jiaotong university

Xiaohua Qian

Xiaohua Qian, Ph.D., joined the BME at Shanghai Jiao Tong University in 2018 as an Associated Professor. Before joining SJTU, Qian worked at The University of Texas Health Science Center at Houston as an Assistant Professor and worked at Wake Forest University’s School of Medicine as a Research Fellow.

Dr. Qian received his Ph.D. in Electronic Engineering from Jilin University School of Electronic Science and Engineering in December 2012. During his doctoral program, he was awarded a full scholarship from the China Scholarship Council and got his academic training on Medical imaging analysis at Duke University Medical Center for two years.

Research

Dr. Qian’s primary research interests and areas of expertise are medical imaging analysis, machine learning and deep learning, and bioinformatics. He has extensive academic and industrial experience in developing biomedical informatics systems, such as automated MDS-UPDRS assessment system for PD, informatics system of pancreatic cancer for diagnosis and treatment, and identification of the DNA methylation cancer biomarkers. Qian develops mathematical and computational models/algorithms to address critical and challenging clinical questions by integrating medical imaging, bioinformatics, and clinical research, finally achieving translation medicine for healthcare.

Monograph

1. X. Chen^#, W.Wang^#, Y. Jiang^#, X. Qian*. A Dual-transformation with Contrastive Learning Framework for Lymph Node Metastasis Prediction in Pancreatic Cancer. Medical Image Analysis, 85: 102753, 2023.

2. J. Qu, X.Wei*, X. Qian*. Generalized Pancreatic Cancer Diagnosis via Multiple Instance Learning and Anatomically-Guided Shape Normalization. Medical Image Analysis, 86: 102774, 2023.

3. R. Guo, H. Li, C. Zhang, X. Qian*. A Tree-Structure-Guided Graph Convolutional Network with Contrastive Learning for the Assessment of Parkinsonian Hand Movements. Medical Image Analysis, 81: 102560, 2022.

4. J. Li^#, L. Qi^#, Q. Chen, Y. Zhang, X. Qian*. A Dual Meta-Learning Framework based on Idle Data for Enhancing Segmentation of Pancreatic Cancer. Medical Image Analysis, 78: 102342, 2022.

5. X. Li^#, R. Guo^#, J. Lu^#, T. Chen, X. Qian*. Causality-Driven Graph Neural Network for Early Diagnosis of Pancreatic Cancer in Non-Contrast Computerized Tomography. IEEE Transactions on Medical Imaging, 42(6): 1656-1667, 2023.

6. X. Song^#, J. Li^#, X. Qian*. Diagnosis of Glioblastoma Multiforme Progression via Interpretable Structure-Constrained Graph Neural Networks. IEEE Transactions on Medical Imaging, 42(2): 380-390, 2023.

7. X. Tang, R. Guo, C. Zhang, X. Zhuang*, X. Qian*. A Causality-driven Graph Convolutional Network for Postural Abnormality Diagnosis in Parkinsonians. IEEE Transactions on Medical Imaging, 08, 2023.

8. X. Tang^#, C. Zhang^#, R. Guo, X. Yang*, X. Qian*. A Causality-Aware Graph Convolutional Network Framework for Rigidity Assessment in Parkinsonians. IEEE Transactions on Medical Imaging, 07, 2023.

9. X. Chen, Z. Chen, J. Li, Y. Zhang, X. Lin, X. Qian*. Model-driven deep learning method for pancreatic cancer segmentation based on spiral-transformation. IEEE Transactions on Medical Imaging, 41(1): 75-87, 2022.

10. X. Chen, X. Lin, Q. Shen, X. Qian*. Combined Spiral Transformation and Model-Driven Multi-Modal Deep Learning Scheme for Automatic Prediction of TP53 Mutation in Pancreatic Cancer. IEEE Transactions on Medical Imaging, 40(2): 735-747, 2021.

11. R. Guo, X. Shao, C. Zhang, X. Qian*. Multi-scale Sparse Graph Convolutional Network for the Assessment of Parkinsonian Gait. IEEE Transactions on Multimedia, 24: 1583-1594, 2022.

12. Z. Xie^#, R. Guo^#, C. Zhang, X. Qian*. A Clinically Guided Graph Convolutional Network for Assessment of Parkinsonian Pronation-Supination Movements of Hands. IEEE Transactions on Circuits and Systems for Video Technology, 09, 2023.

13. R. Guo, J. Sun, C. Zhang, X. Qian*. A Contrastive Graph Convolutional Network for Toe-Tapping Assessment in Parkinson’s Disease. IEEE Transactions on Circuits and Systems for Video Technology, 32(12): 8864-8874, 2022.

14. R. Guo^#, J. Sun^#, C. Zhang, X. Qian*. A Self-Supervised Metric Learning Framework for the Arising-from-Chair Assessment of Parkinsonians with Graph Convolutional Networks. IEEE Transactions on Circuits and Systems for Video Technology, 32(9): 6461-6471, 2022.

15. R. Guo, X. Shao, C. Zhang, X. Qian*. Sparse Adaptive Graph Convolutional Network for Leg Agility Assessment in Parkinson’s Disease. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(12): 2837-2848, 2020.

16. J. Li^#, T. Chen^#, X. Qian*. Generalizable Pancreas Segmentation Modeling in CT Imaging via Meta-learning and Latent-space Feature Flow Generation. IEEE Journal of Biomedical and Health Informatics, 27(1): 374-385, 2023.

17. J. Li^#, H. Zhu^#, T. Chen*, X. Qian*. Generalizable Pancreas Segmentation via a Dual Self-Supervised Learning Framework. IEEE Journal of Biomedical and Health Informatics, 27(10): 4780-4791, 2023.

18. J. Li, C. Feng, X.Lin, X. Qian*. Utilizing GCN and Meta-Learning Strategy in Unsupervised Domain Adaptation for Pancreatic Cancer Segmentation. IEEE Journal of Biomedical and Health Informatics, 26(1): 79-89, 2022.

19. X. Song^#, M. Mao^#, X. Qian*. Auto-Metric Graph Neural Network Based on a Meta-learning Strategy for the Diagnosis of Alzheimer's disease. IEEE Journal of Biomedical and Health Informatics, 25(8): 3141-3152, 2021.

20. G. Xu, J. Reboud, Y. Guo, H. Yang, H. Gu, C. Fan*, X. Qian*, Jonathan M Cooper*. Programmable design of isothermal nucleic acid diagnostic assays through abstraction-based models. Nature communications, 13(1): 1-9, 2022. (共同通讯)

Contact

E-mail：xiaohua.qian@sjtu.edu.cn

telephone：021-62932187

address：徐汇校区教三楼南楼421室