Prof YUAN, Yixuan 袁奕萱

Assistant Professor
B.Eng. (NWPU, China), Ph.D. (CUHK)
homepage Rm 311, Ho Sin Hang Engineering Building phone Tel: +852 3943 4461 This email address is being protected from spambots. You need JavaScript enabled to view it.

Research Interests:

Medical image analysis, Deep learning in healthcare, Abnormality detection
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hyperlink http://www.ee.cuhk.edu.hk/~yxyuan/

 

  

Resume of Career

Yixuan Yuan received the bachelor’s degree in Automation from Northwestern Polytechnical University (NPU), China in 2010, the doctorate degree in Electronic Engineering from the Chinese University of Hong Kong (CUHK) in 2016 with Hong Kong Postgraduate Fellowship (HKPFS). She was a visiting student in Radiology at Stanford University from 2014 to 2015. She was a postdoctoral Fellow in the Department of Radiation Oncology, Stanford Cancer Center, Stanford University, supervised by Prof. Lei Xing during 2017-2018. From 2018-2022, she was an assistant professor in the Department of Electrical Engineering at the City University of Hong Kong. She is currently an assistant professor in the Department of Electronic Engineering at the Chinese University of Hong Kong.

Research Interests

Medical image analysis, Deep learning in healthcare, Abnormality detection

Publications for the Past 3 Years

  1. Q. Yang, X. Guo, Z. Chen, Peter Y. M. Woo, Y. Yuan. “D2-Net: Dual Disentanglement Network for Brain Tumor Segmentation with Missing Modalities.” IEEE Transactions on Medical Imaging (TMI), 41(10), 2953 – 2964, 2022.
  2. X. Liu, Y. Yuan. “Domain Adaptive Polyp Detection and Style Restoration Framework without Source Data.” IEEE Transactions on Medical Imaging (TMI), 41 (7), 1897-1908, 2022.
  3. X. Guo, J. Liu, Y. Yuan. “Semantic-oriented Labeled-to-unlabeled Distribution Translation for Image Segmentation.” IEEE Transactions on Medical Imaging (TMI), 41(2):434-445, 2022.
  4. C. Yang, X. Guo, Z. Chen, Y. Yuan. “Source Free Domain Adaptation for Medical Image Segmentation with Fourier Style Mining.” Medical Image Analysis (MedIA), 102457, 2022.
  5. W. Li, X. Liu, Y. Yuan. “SIGMA: Semantic-complete Graph Matching For Domain Adaptative Object Detection.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR-22, ORAL, Best Paper Finalist).
  6. X. Guo, J. Liu, T. Liu, Y. Yuan. “SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR-22).
  7. X. Liu, W. Li, Q. Yang, B. Li, Y. Yuan. “Towards Robust Adaptive Object Detection under Noisy Annotations.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR-22).
  8. W. Li, Z. Chen, B. Li, D. Zhang, Y. Yuan. “HTD: Heterogeneous Task Decoupling for Two-stage Object Detection.” IEEE Transactions on Image Processing (TIP), 30:9456-9469, 2021.
  9. M. Zhu, Z. Chen, Y. Yuan, “DSI-Net: Deep Synergistic Interaction Network for Joint Classification and Segmentation with Endoscope Images.” IEEE Transactions on Medical Imaging (TMI), 40(12):3315-3325, 2021.
  10. X. Guo, C. Yang, Y. Yuan, “Dynamic-weighting Hierarchical Segmentation Network for Medical Images.” Medical Image Analysis (MedIA). 73, 102196, 2021.
  11. X. Liu, X. Guo, Y. Liu, Y. Yuan, “Consolidated Domain Adaptive Detection and Localization Framework for Cross-device Colonoscopic Images.” Medical Image Analysis (MedIA), 71, 102052, 2021. 
  12. Z. Chen, M. Zhu, C. Yang, Y. Yuan, “Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning.” The 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). (MICCAI 2021 Student Travel Award, Oral)
  13. X. Guo, C. Yang, B. Li, Y. Yuan, “MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR-21).
  14. Z. Chen, J. Zhang, S. Che, J. Huang, X. Han, Y. Yuan, “Diagnose Like A Pathologist: Weakly-Supervised Pathologist-Tree Network for Slide-Level Immunohistochemical Scoring.” The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021).
  15. X. Liu, B. Li, Z. Chen, Y. Yuan, “Exploring Gradient Flow Based Saliency for DNN Model Compression." The 29th ACM International Conference on Multimedia (ACM MM 2021)