Xiaoying Tang, PhD from Johns Hopkins University, Tenured Associate Professor and Researcher in the Department of Electronic and Electrical Engineering at the Southern University of Science and Technology. She is also a Visiting Professor at Johns Hopkins University and Carnegie Mellon University. She has been recognized as a Guangdong Province Distinguished Young Scholar, Shenzhen City Distinguished Young Scholar, and high-level overseas talent recruited by Shenzhen City. Additionally, she has served as the principal investigator for more than 10 research projects, including the Youth Scientists Project of the Ministry of Science and Technology's Key Research and Development Program during the 14th Five-Year Plan and the Key Research and Development Program Project during the 13th Five-Year Plan. In addition, she serves as an editorial board member for the journals Neural Networks and IEEE Transactions on Cognitive and Developmental Systems. She has held various leadership roles in the field of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), including chairing sessions, being a track chair, and serving as the local chair of the conference. She is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and the Chinese Society of Biomedical Engineering. Furthermore, she serves as the Secretary-General of the IEEE EMBS Shenzhen Chapter.

Education

• 2009-2014  Johns Hopkins University (USA), Ph.D. Electrical and Computer Engineering

• 2013-2014  Johns Hopkins University, M.S. Applied Mathematics and Statistics

• 2009-2011  Johns Hopkins University, M.S. Electrical and Computer Engineering

• 2005-2009  Huazhong University of Science and Technology, B.E. Control System Engineering

• 2006-2009  Huazhong University of Science and Technology, B.S. Foreign Language Education

Working Experiences

• 2024/01–NOW   Tenured Associate Professor, Researcher, Electrical and Electronic Engineering, Southern University of Science and Technology

• 2018/05–2023/12   Tenure-track Assistant Professor, Associate Researcher, Electrical and Electronic Engineering, Southern University of Science and Technology

• 2016/02–NOW   Adjunct Assistant Professor, Electrical and Computer Engineering, Carnegie Mellon University (USA)

• 2015/05–NOW   Adjunct Assistant Professor, Electrical and Computer Engineering, Johns Hopkins University (USA)

• 2015/05–2018/05 Associate Professor, School of Electronics and Information Technology, Sun Yat-sen University (formerly named School of Mobile and Information Engineering)

• 2015/05–2018/05 Tenure-track Assistant Professor, SYSU-CMU Joint Institute of Engineering, Sun Yat-sen University

• 2015/05–2018/05 Assistant Professor, SYSU-CMU Shunde International Joint Research Institute

• 2015/05–2016/08  Visiting Professor, Electrical and Computer Engineering, Carnegie Mellon University (USA)

• 2014/05–2015/05  Postdoctoral Research Fellow, Center for Imaging Science, Johns Hopkins University (USA)

Research Introduction

Dr. Tang’s research focuses on the intelligent and quantitative analysis of medical images, with primary applications to the multi-modality MRI image analysis of the human brain and the multi-modality ophthalmic images, as well as the AI-based early diagnosis and prediction of brain disorders and eye diseases.

Research Interests：

Medical image segmentation and registration, diffusion tensor image analysis, statistical shape analysis, manifold learning and clustering, spatiotemporal analysis, multi-modality MRI analysis, pattern recognition, machine learning, big data in medicine.

Papers

1. L. Lin, L. Peng, H. He, P. Cheng, J. Wu, K. K. Y. Wong, and X. Tang*, “YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation”, Medical Image Analysis, 90: 102937, 2023.

2. Z. Wang, J. Lyu, and X. Tang*, “autoSMIM: Automatic Superpixel-based Masked Image Modeling for Skin Lesion Segmentation”, IEEE Transactions on Medical Imaging, 42(12): 3501-3511, 2023.

3. P. Cheng, L. Lin, J. Lyu, Y. Huang, W. Luo, and X. Tang*, “PRIOR: Prototype Representation Joint Learning from Medical Images and Reports”, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Paris, August 2023.

4. X. Liu, X. Wang, Y. Luo, M. Wang, Z. Chen, X. Han, S. Zhou, J. Wang, J. Kong, X. Wang, X. Tang*, and Q. Guo*, “A 3D Tumor-mimicking in Vitro Drug Release Model of Locoregional Chemoembolization Using Deep Learning-based Quantitative Analyses”, Advanced Science, 10(11): 2206195, 2023.

5. J. Lyu, Y. Zhang, Y. Huang, L. Lin, P. Cheng, and X. Tang*, “AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation”, IEEE Transactions on Medical Imaging, 41(12): 3699-3711, 2022.

6. J. Wu and X. Tang*, “Brain Segmentation Based on Multi-atlas and Diffeomorphism Guided 3D Fully Convolutional Network Ensembles”, Pattern Recognition, 115: 107904, 2021.

7. Y. Zhang, P. Zhong, D. Jie, J. Wu, S. Zeng, J. Chu, Y. Liu, E.X. Wu, and X. Tang*, “Brain Tumor Segmentation from Multi-modal MR Images via Ensembling UNets”, Frontiers in Radiology, 1: 704888, 2021.

8. Y. Zhang, J. Wu, Y. Liu, Y. Chen, E.X. Wu, and X. Tang*, “MI-UNet: Multi-Inputs UNet Incorporating Brain Parcellation for Stroke Lesion Segmentation from T1-weighted Magnetic Resonance Images”, IEEE Journal of Biomedical and Health Informatics, 25(2): 526-535, 2021.

9. L. Zou, Y. Song, X. Zhou, J. Chu*, and X. Tang*, “Regional Morphometric Abnormalities and Clinical Relevance in Wilson’s Disease”, Movement Disorders, 34(4): 545-554, 2019.

10. X. Tang*, C.A. Ross, H. Johnson, J.S. Paulsen, L. Younes, R.L. Albin, J.T. Ratnanather, and M.I. Miller, “Regional Subcortical Shape Analysis in Premanifest Huntington’s Disease”, Human Brain Mapping, 40(5): 1419-1433, 2019.

Publications

1. M.I. Miller, S. Mori, X. Tang, D. Tward, and Y. Zhang, “3D Deformable templates in computational anatomy”. In: A.W. Toga, Eds. Brain Mapping: An Encyclopedic Reference, Elsevier, Academic Press, 2015.

Recruitment Announcement

Dr. Tang’s group recruits research assistants professors, post-doc researchers, research assistants, Ph.D. students, master students, as well as undergraduate interns. Visiting scholars and students are also welcome. Please contact Dr. Tang directly via email if interested in any of the above positions.

Contact Information

Email: [email protected]