Hongming Shan, PhD

Associate Professor
Institute of Science and Technology for Brain-inspired Intelligence
Fudan University
Shanghai Center for Brain Science and Brain-inspired Technology

I'm an Associate Professor at the Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, and also a "Qiusuo" Research Leader with the Shanghai Center for Brain Science and Brain-inspired Technology. Before joining Fudan University, I was a Research Scientist working with Prof. Ge Wang at Biomedical Imaging Center, Rensselaer Polytechnic Institute. I received my Ph.D. in machine learning from Fudan University in 2017, under the supervision of Prof. Junping Zhang.

Research

My research interests are in machine learning, computer vision, and their applications to medical image reconstruction and processing.
My primary interests are deep learning, medical image analysis, and neurodegenerative disorders. Recently, I have been working on low-dose CT denoising, reconstruction, and diagnosis, super-resolution, radiomics, metal artifacts reduction, motion artifact reduction, medical image segmentation, radiotherapy, face aging, age estimation, and ordinal regression.

Selected Publications

See Publications or Google Scholar for a full list of publications.

Z. Huang, S. Chen, J. Zhang and H. Shan. PFA-GAN: Progressive Face Aging with Generative Adversarial Network. IEEE Transactions on Information Forensics and Security, 2021
H. Shan, X. Jia, P. Yan, Y. Li, H. Paganetti and G. Wang. Synergizing Medical Imaging and Radiotherapy with Deep Learning. Machine Learning: Science and Technology, 1(2), 021001, 2020
H. Shan, A. Padole, F. Homayounieh, U. Kruger, R. D. Khera, C. Nitiwarangkul, M. K. Kalra and G. Wang. Competitive Performance of a Modularized Deep Neural Network Compared to Commercial Algorithms for Low-Dose CT Image Reconstruction. Nature Machine Intelligence, 1, 269–276, 2019
- Press: NIH/NIBIB, RPI News, EurekAlert!, Physicsworld, HealthImaging, Value Walk, and so on.
H. Shan, J. Zhang and U. Kruger. Framework of Randomized Distribution Features for Visual Representation and Categorization. IEEE Transactions on Cybernetics, 49(9), 3599-3606, 2019
H. Shan, Y. Zhang, Q. Yang, U. Kruger, M. K. Kalra, L. Sun, W. Cong and G. Wang. 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network. IEEE Transactions on Medical Imaging, 37(6), 1522-1534, 2018
- Press: Lindau Nobel Laureate Meetings
H. Shan, J. Zhang and U. Kruger. Learning Linear Representation of Space Partitioning Trees based on Unsupervised Kernel Dimension Reduction. IEEE Transactions on Cybernetics, 46(12), 3427-3438, 2016

Professional Service

Associate Editor:	The 21st IEEE International Conference on Intelligent Transportation Systems (IEEE-ITSC 2018)
Program Committee:	The 10th International Workshop on Machine Learning in Medical Imaging (MLMI 2019), The 11th International Workshop on Machine Learning in Medical Imaging (MLMI 2020), IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020)
Coordinator:	Deep Reconstruction Workshop, Deep Learning Journal Club
Reviewer:	ECML 2014, ACML 2015, ACML 2016, ICASSP 2018, MICCAI 2019, MICCAI 2020 IEEE Transactions on Medical Imaging, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Intelligent Transportation Systems, IEEE Intelligent Systems, Machine Learning, IEEE Access, Pattern Recognition, Medical Physics, Physics in Medicine and Biology, Machine Vision and Applications, IEEE Journal of Selected Topics in Signal Processing, Scientific Reports, Photoacoustics, etc.

Contact

Email:	"{}@{}".format("hmshan", "fudan.edu.cn")
Office:	Room 2406, East Main Building of Guanghua Tower
Address:	Institute of Science and Technology for Brain-inspired Intelligence Fudan University 220 Handan Rd., Yangpu District, Shanghai 200433, China