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. 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 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.

1.   J. Li#, T. Chen#, X. Qian*Generalizable Pancreas Segmentation Modeling in CT Imaging via Meta-learning and Latent-space Feature Flow GenerationIEEE Journal of Biomedical and Health Informatics, 09, 2022.

2.   X. Song#, J. Li#X. Qian*Diagnosis of Glioblastoma Multiforme Progression via Interpretable Structure-Constrained Graph Neural Networks. IEEE Transactions on Medical Imaging, 08, 2022.

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, 07, 2022.

4.  R. Guo, J. Sun, C. Zhang, X. Qian*A Contrastive Graph Convolutional Network for Toe-Tapping Assessment in Parkinson's DiseaseIEEE Transactions on Circuits and Systems for Video Technology, 07, 2022.

5.  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 NetworksIEEE Transactions on Circuits and Systems for Video Technology, 03, 2022.

6.  J. Li#, L. Qi#, Q. Chen, Y. Zhang, X. Qian*A Dual Meta-Learning Framework based on Idle Data for Enhancing Segmentation of Pancreatic CancerMedical Image Analysis, v.78, 2022

7.  X. Chen, Z. Chen, J. Li, Y. Zhang, X. Lin, X. Qian*Model-driven Deep Learning Method for Pancreatic Cancer Segmentation Based on Spiral-transformationIEEE Transactions on Medical Imaging, 41(1), 2022.

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

9.  R. Guo, X. Shao, C. Zhang, X. Qian*Multi-scale Sparse Graph Convolutional Network for the Assessment of Parkinsonian GaitIEEE Transactions on Multimedia, v.24, 2022.

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

11.  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 CancerIEEE Transactions on Medical Imaging, 40(2), 2021.

12.  R. Guo, X. Shao, C. Zhang, X. Qian*Sparse Adaptive Graph Convolutional Network for Leg Agility Assessment in Parkinson’s DiseaseIEEE Transactions on Neural Systems & Rehabilitation Engineering, 28(12),2020.

13  G. Xu, J. Reboud, Y. Guo, H. Yang, H. Gu, C. Fan*, X. Qian*, J. M. Cooper*. Programmable design of isothermal nucleic acid diagnostic assays through abstraction-based modelsNature Communications, 13(1), 2022.