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. Prior to that, Qian was a Research Assistant Professor at Shanghai Advanced Research Institute, Chinese Academy of Sciences. 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.

Dr. Qian’s primary research interests and areas of expertise are medical imaging analysis, machine learning and deep learning, and data mining in healthcare. 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.

2009 – 2012, Ph.D., Department of Electrical Engineering, Jilin University

2006 – 2009, Master, Department of Electrical Engineering, Jilin University

2003 – 2005, Bachelor, Department of Business Administration, Jilin University

2002 – 2006, Bachelor, Department of Electrical Engineering, Jilin University

2018-present, Associated professor, School of Biomedical Engineering, Shanghai Jiao Tong University, China

2017-2017, Assistant Professor, School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA

2014-2017, Research Fellow, Department of Radiology, Wake Forest University, USA

2013-2014, Research Assistant Professor, Chinese Academic of Science, China

2010-2012, Visiting scholar, Department of Medical Physics, Duke University, USA

Machine learning and deep learning, medical image analysis, and data mining in healthcare

Data structure, Spring, 2019-

Data mining in BME, Spring, 2022-

1. 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, 08, 2021.
2. J. Li, C. Feng, Q. Shen, X. Lin, X. Qian*. Pancreatic Cancer Segmentation in Unregistered Multi-parametric MRI with Adversarial Learning and Multi-scale Supervision. Neurocomputing, 09,2021
3. 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, 05, 2021.
4. R. Guo, X. Shao, C. Zhang, X. Qian*. Multi-scale Sparse Graph Convolutional Network for the Assessment of Parkinsonian Gait. IEEE Transactions on Multimedia, 04, 2021.
5. J. Li, X. Zhu, H. Chen, H. Li, X. Qian*. Pancreas Segmentation with Probabilistic Map Guided Bi-directional Recurrent U-Net. Physics in Medicine and Biology, 04, 2021.
6. 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, 03, 2021(Source code is available at https://github.com/SJTUBME-QianLab/AutoMetricGNN)
7. H. Li, X. Shao, C. Zhang, X. Qian*. Automated Assessment of Parkinsonian Finger-tapping Tests through a Vision-based Fine-grained Classification Model. Neurocomputing, 03, 2021.
8. 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), 2021.
9. 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 & Rehabilitation Engineering, 28(12),2020.
10. X. Liu, X. Zhou, X. Qian*. Transparency-guided ensemble convolutional neural network for the stratification between pseudoprogression and true progression of glioblastoma multiform in MRI. Journal of Visual Communication and Image Representation, vol.72, 2020
11. M. Li, M. D. Chan, X. Zhou, and X. Qian*. DC-Al GAN: Classification of Pseudoprogression and True Tumor Progression of Glioblastoma multiform Based on DCGAN and AlexNet. Medical Physics, 12, 2019.
12. J. Wang, H. Liu, X. Qian*, et al. Cascaded Hidden Space Feature Mapping, Fuzzy Clustering, and Nonlinear Switching Regression on Large Datasets. IEEE Transactions on Fuzzy Systems, 26(2), 2018.
13. C. Sun, S. Guo, H. Zhang, J. Li, S. Ma, L. Jin, X. Liu, X. Li*, X. Qian*. Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs. Artificial Intelligence in Medicine, vol.83, 2017.
14. X. Qian, H. Tan, J. Zhang, et al. Stratification of Pseudoprogression and True Progression of Glioblastoma Multiform Based on Longitudinal Diffusion Tensor Imaging without Segmentation. Medical Physics, 43(11), 2016.
15. X. Qian, H. Tan, J. Zhang, et al. Objective classification system for sagittal craniosynostosis based on suture segmentation. Medical Physics, 42(9), 2015.
16. X. Qian, Y. Lin, J. Wang, et al. Segmentation of myocardium from cardiac MR images using a novel dynamic programming based segmentation method. Medical Physics, 42(3), 2015.
17. X. Qian, J. Wang, S. Guo, et al. An Active Contour Model for Medical Image Segmentation with Application to Brian CT Image. Medical Physics, 2013, 40(2).