Yutong Xie
Assistant Professor of Computer Vision
Research Interests
Professor Yutong Xie's research primarily focuses on computer vision and machine learning for healthcare sector, aiming to develop intelligent solutions to assist healthcare professionals in anatomical structure segmentation, disease diagnosis, prognosis, and therapy, particularly in the context of medical data analysis with limited annotations.
Prior to joining MBZUAI, Xie was a Research Fellow at the University of Adelaide (UoA), and a member of the Australian Institute for Machine Learning (AIML). She has published more than 50 peer-reviewed publications, most in flagship journals/conference proceedings including IEEE-TPAMI/TMI/TIP, IJCV, Medical Image Analysis, IEEE-TCSVT, MICCAI, CVPR, ECCV, IJCAI, ACCV. Her research has attracted over 5,000 Google citations. Xie received the Outstanding Doctoral Dissertation Award and the CVPR 2024 DEI Grants. She serves as an Area Chair of MICCAI 2023/2024/2025. She is also a reviewer of flagship journals/conference proceedings (e.g., IEEE-TPAMI/TMI/TIP, MedIA, NeurIPS, ICLR, CVPR, ICCV, ECCV, MICCAI, and AAAI) and has received the Distinguished Reviewer Certificate from IEEE-TMI and CVPR 2023.
- Research Fellow, Australian Institute for Machine Learning, University of Adelaide, Australia
- 2022-2024, Top 2% Scientists Worldwide, Stanford University
- 2024, CVPR 2024 DEI grants
- 2023, Distinguished Reviewer of IEEE-TMI
- 2023, Outstanding Reviewer of CVPR 2023
- Xie Y, Zhang J, Xia Y, et al. UniMiSS+: Universal Medical Self-Supervised Learning From Cross-Dimensional Unpaired Data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
- Xie Y, Chen Q, Wang S, et al. PairAug: What Can Augmented Image-Text Pairs Do for Radiology?[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 11652-11661.
- Xie Y, Gu L, Harada T, et al. Rethinking masked image modelling for medical image representation[J]. Medical Image Analysis, 2024, 98: 103304.
- Xie Y, Zhang J, Liu L, et al. ReFs: A hybrid pre-training paradigm for 3D medical image segmentation[J]. Medical Image Analysis, 2024, 91: 103023.
- Xie Y, Zhang J, Xia Y, et al. Learning from partially labeled data for multi-organ and tumor segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.