薛澄（Cheng Xue）
Ph.D., Associate Professor
School of Computer Science and Engineering, Southeast University
Email: cxue AT seu.edu.cn
Office: Room 408, School of Computer Science and Engineering, Southeast University Jiulonghu Campus, Nanjing, Jiangsu, China.
個人簡介 / Biography
I am a tenure-track associate professor in the School of Computer Science and Engineering at Southeast University. Before that, I was a Research Assistant Professor at Hong Kong Polytechnic University and a postdoctoral researcher in the Department of Computer Science and Engineering at the Chinese University of Hong Kong advised by Prof. Pheng Ann Heng. My research interest lies in deep learning and medical image analysis.
歡迎2024年入學的研究生與我郵件聯系。
歡迎對AI+醫學影像感興趣的本科生跟組進行全面的科研訓練。
學習經曆 / Education
Ph. D., Department of Health Technology and Informatics, Hong Kong Polytechnic University. (2012 - 2017)
B. Eng., Department of Mechanical Engineering and Automation, Jilin University. (2008 - 2012)
工作經曆 / Experience
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)
代表性論著 / Selected publications
Cheng Xue, Lequan Yu, Pengfei Chen, Qi Dou, Pheng-Ann Heng. Robust medical image classification from noisy labeled data with global and local representation guided co-training. IEEE Transactions on Medical Imaging 41, no. 6 (2022): 1371-1382. (JCR Q1)
Cheng Xue, Lei Zhu, Huazhu Fu, Xiaowei Hu, Xiaomeng Li, Hai Zhang, Pheng-Ann Heng, Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images, Medical image analysis, 70, 101989. (JCR Q1)
Cheng Xue, Qiao Deng, Xiaomeng Li, Qi Dou, and Pheng-Ann Heng. Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI),pp. 579-588. Springer, Cham, 2020.
Cheng Xue, Qi Dou, Xueying Shi, Hao Chen, and Pheng-Ann Heng. Robust learning at noisy labeled medical images: Applied to skin lesion classification. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI), pp. 1280-1283. IEEE, 2019.
Fuk-hay Tang, Cheng Xue, Maria YY Law, Chui-ying Wong, Tze-hei Cho, and Chun-kit Lai. Prognostic Prediction of Cancer Based on Radiomics Features of Diagnostic Imaging: The Performance of Machine Learning Strategies. Journal of Digital Imaging (2023): 1-10. (JCR Q1)