薛澄，副教授，硕导。2017年获得香港理工大学博士学位，期间在美国杜克大学进行学术访问。2019-2023年在香港中文大学和香港理工大学担任博士后、研究助理教授。近期研究方向为：医学影像智能分析，手术机器人，多模态大模型，具身智能等。在医学影像分析领域顶刊和顶会TMI，MIA，MICCAI等发表论文二十余篇，长期担任多个国际著名期刊会议的编委和审稿人。
1. 招收2027级计算机学院/软件学院硕士研究生，欢迎有自我驱动力的学生报名！
2. 欢迎对AI+医学影像感兴趣的本科生跟组进行全面的科研训练。
3. To date, I have no quotas for overseas students.
[02/2026] Two papers accepted to CVPR 2026( Congrats to Lu Niu and Zining)
[01/2026] One paper accepted to ICASSP 2026 as oral (Congrats to my undergraduate student Yuxin)
[11/2025] One paper accepted to IEEE JBHI 2025 (JCR Q1) (Congrats to Shiyu)
医学影像智能分析，多模态大模型，具身智能等
Ph. D., Department of Health Technology and Informatics, Hong Kong Polytechnic University. (2012 - 2017)
Visiting Scholar, Department of Electrical and Computer Engineering, Duke University. (2015-2016)
B. Eng., Department of Mechanical Engineering and Automation, Jilin University. (2008 - 2012)
Tenure-Track Associate Professor, School of Computer Science and Engineering, Southeast University. (2023 - present)
Research Assistant Professor, Department of Health Technology and Informatics, Hong Kong Polytechnic University. (2022)
Postdoctoral Researcher, Department of Computer Science and Engineering, Chinese University of Hong Kong (2017 - 2022)
面向标注不一致与分布不均衡数据的医学影像带噪学习算法研究，国家自然科学基金青年科学基金项目（C类），2025年1月至2027年12月，主持
面向医学影像数据复杂噪声的鲁棒学习算法研究，江苏省基础研究计划青年项目，2024年9月至2027年8月，主持
海外博士后引才专项，2023年至2025年，主持
基于多模态影像再生技术的腔内肿瘤多维特征刻画研究，国家重点研发计划，2024年12月至2027年11月，参与
基于影像和多组学的冠心病精准诊疗和风险预测智能模型构建及应用研究，国家自然科学基金专项项目，2025年1月至2027年12月，参与
Niu, L., Xue, C†., “Noise-Aware Few-Shot Learning through Bi-directional Multi-view Prompt Alignment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026.
Fang, Z.*, Xue, C.*†, Liu, C., Xu, B., Chen, M., Hu, X. “PhySe-RPO: Physics and Semantics Guided Relative Policy Optimization for Diffusion-Based Surgical Smoke Removal,” in Findings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026.
He, Y., Li, A., Xue, C.†, “CauCLIP: Bridging the Sim-to-Real Gap in Surgical Video Understanding via Causality-Inspired Vision-Language Modeling,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2026.
Xue, C., Zhao, S., Wang, D., Chen, C., Yang, G., Chen, Y., “TD-SAM: Temporal and Distance-Guided Adaptations of SAM for Accurate Surgical Instrument Segmentation,” IEEE Journal of Biomedical and Health Informatics, 2025, accepted.
Zhang, Q., Li, Y., Xue, C., Wang, H., Li, X., “GlandSAM: Injecting Morphology Knowledge into Segment Anything Model for Label-Free Gland Segmentation,” IEEE Transactions on Medical Imaging, 2024.
Zhang, S., Chen, M., Wu, J., Zhang, Z., Li, T., Xue, C.†, Kong, Y.†, “Spineclue: Automatic Vertebrae Identification Using Contrastive Learning and Uncertainty Estimation,” Artificial Intelligence in Medicine, 2024.
Xue, C., Yu, L., Chen, P., Dou, Q., Heng, P.-A., “Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training,” IEEE Transactions on Medical Imaging, 2022.
Xue, C., Zhu, L., Fu, H., Hu, X., Li, X., Zhang, H., Heng, P.-A., “Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images,” Medical Image Analysis, 2021.
Xue, C., “DIER-Net: Debiased Learning with Medical Image Noisy Label by Intrinsic and Extrinsic Regularization,” International Journal of Imaging Systems and Technology, 2025.
Zhang, Q., Li, Y., Xue, C., Li, X., “Morphology-Inspired Unsupervised Gland Segmentation via Selective Semantic Grouping,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023.
Chen, X., He, Y., Xue, C., Ge, R., Li, S., Yang, G., “Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-training,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023.
Xue, C., Deng, Q., Li, X., Dou, Q., Heng, P.-A., “Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020.
Xue, C., Dou, Q., Shi, X., Chen, H., Heng, P.-A., “Robust Learning from Noisy Labeled Medical Images: Applied to Skin Lesion Classification,” in IEEE International Symposium on Biomedical Imaging (ISBI), 2019.
* co first author; † corresponding author.
