• MEng: 1) Electronic Information

• MS：1)Intelligent Science and Technology

• Ph.D: 1) Electronic Information; 2) Intelligent Science and Technology;

Xu Yanwu, a tenured professor and doctoral supervisor at the School of Future Technology, South China University of Technology, focuses on research in artificial intelligence and medical imaging. Expert of the WHO Digital Health Advisory Committee, SERI Visiting Researcher of the Singapore Eye Institute, Senior Member of IEEE, Member of the Science and Technology Innovation and Industry Promotion Working Committee of the Chinese Society of Biomedical Engineering, Standing Committee Member of the Intelligent Ophthalmology Branch and the Digital Therapeutics Working Committee of the China Medical Education Association Member of the Big Data and Internet Artificial Intelligence Medical Specialized Committee of the Beijing Health Law Society. Mainly engaged in research on the theories and applications of computer vision and machine learning, he/she has published over 140 papers in international journals and conferences, with more than 6,300 citations on Google. He/She has also applied for over 20 international patents and over 70 Chinese patents. Currently serves as an editorial board member of Medical Imaging and BioMedical Engineering Online journals under Springer Nature. Founding editorial board member of Intelligent Medicine, an English journal under the 'China Science and Technology Journal Excellence Action Plan' hosted by the Chinese Medical Association, and guest editor of several SCI journals. Served as a member of the organizing committee of the top medical imaging conferences MICCAI and IPMI, as well as a member of the organizing committee and PC of international academic conferences such as AAAI, ACPR, and ACCAS, and was the founding chairperson of the international conference on ophthalmic medical imaging OMIA and the international competition platform iChallenge. He/She was successively appointed as a specially-appointed expert under the Ministry of Public Security's Talent Introduction Program, a specially-appointed expert of Zhejiang Province, and a specially-appointed expert of Beijing Municipality. Welcome to sign up and contact: xuyanwu@scut.edu.cn

• Artificial Intelligence and Medical Imaging Research

• Fang, H., Yin, P., Chen, H., Fang, Y., Chen, W., Yuan, J., Risa, H., Liu, J. L. & Xu, Y.* (2022). Lens structure segmentation from as-oct images via shape-based learning. Computer Methods and Programs in Biomedicine (CMPB), IF2021=7.027, JCR-Q1, 107322

• Zhang, X., …, Xu, Y.*, Higashita, R. & Liu, J. (2022). Attention to region: Region-based integration-and-recalibration networks for nuclear cataract classification using as-oct images. Medical Image Analysis (MedIA), IF2021=13.828, JCR-Q1, 102499

• Fang, H., Li, F., ..., Zhang, X. & Xu, Y.* (2022). Adam challenge: Detecting age-related macular degeneration from fundus images. IEEE Transactions on Medical Imaging (TMI), IF2021=11.037, JCR-Q1, 41(10), 2828–2847

• Li, F., ..., Xu, Y.*, Ting, D. S. & Zhang, X. (2022). Digital gonioscopy based on three-dimensional anterior segment optical coherence tomography: An international multicenter study. Ophthalmology, IF2021=14.277, JCR-Q1, 129(1):45-53

• Yang, Y., ..., Xu, Y*., Zhang, W. & Zhang, T. (2021). Robust collaborative learning of patch-level and image-level annotationsfor diabetic retinopathy grading from fundus image. IEEE Transactions on Cybernetics (T-CYB), IF2019=11.079, JCR-Q1

• Lei, B., Cheng, N., Tan, E. -L., Frangi, F. A., Yang, P., Elazab, A., Du, J., Xu, Y.* & Wang, T. (2020). Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early  alzheimer’s disease. Medical Image Analysis (MedIA), IF2020=8.545, JCR-Q1, 61, 101652.

• Yin, P., Xu, Y.*, Zhu, J., Liu, J., Yi, C., Huang, H. & Wu, Q. (2021). Deep level set learning for optic disc and cup segmentation. Neurocomputing (NC), IF2020=5.719, JCR-Q1, 464, 330–341.

• Fu, H., Li, F., Sun, X., Cao, X., ..., Zhang, X. & Xu, Y. *(2020). Age challenge: Angle closure glaucoma evaluation in anterior segment optical coherence tomography. Medical Image Analysis (MedIA), IF2020=8.545, JCR-Q1, 66, 101798.

• Fu, H., Xu, Y.*, Lin, S., Wong, D. W. K., Baskaran, M., Mahesh, M., Aung, T. & Liu, J. (2020). Angle-closure detection in anterior segment oct based on multi-level deep network. IEEE Transactions on Cybernetics (T-CYB), IF2020=11.448, JCR-Q1, 50(7), 3358–3366.

• Fu, H., Baskaran, M., Xu, Y.*, Lin, S., Wong, D. W. K., Liu, J., Mahesh, M., Perera, S. A. & Aung, T. (2019). A deep learning system for automated angle-closure detection in anterior segment optical coherence tomography images. American Journal of Ophthalmology (AJO), IF2020=5.258, JCR-Q1, 201, 37–45.

• Fu, H., Cheng, J., Xu, Y.*, Wong, D. W. K., Liu, J. & Cao, X. (2018). Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Transactions on Medical Imaging (TMI), IF2020=10.048, JCR-Q1, 37(7), 1597–1605.

• Fu, H., Cheng, J., Xu, Y.*, Zhang, C., Wong, D. W. K., Liu, J. & Cao, X. (2018). Disc-aware ensemble network for glaucoma screening from fundus image. IEEE Transactions on Medical Imaging (TMI), IF2020=10.048, JCR-Q1, 37(11), 2493–2501.

• Fu, H., Xu, Y., Lin, S., Zhang, X., Wong, D. W. K., Liu, J., Frangi, A. F., Baskaran, M. & Aung, T. (2017). Segmentation and quantification for angle-closure glaucoma assessment in anterior segment oct. IEEE transactions on medical imaging (TMI), IF2020=10.048, JCR-Q1, 36(9), 1930–1938.

• Guo, Y., Chen, Q., Cao, J., ...,Xu, Y.* & Tan, M. (2020). Closed-loop matters: Dual regressionnetworks for single image super-resolution, In Ieee/cvf conference on computer vision and pattern recognition (cvpr).

• Yan, Y., Tan, M., Xu, Y.*, Cao, J., Ng, K.-P. M., Min, H. & Wu, Q. (2019). Oversampling forimbalanced data via optimal transport, In Thirty-third aaai conference on artificial intelligence (aaai-19).