Hangfan Liu, Ph.D.
Postdoctoral Researcher
Artificial Intelligence in Biomedical Imaging Lab (AIBIL)
Center for Biomedical Image Computing & Analytics (CBICA)
Department of Radiology
Perelman School of Medicine
University of Pennsylvania
Richards Labs, Suite 700D
3700 Hamilton Walk
Philadelphia, PA 19104
Email
Educational Background
Ph.D. in Computer Science, Peking University
Research Summary
Hangfan Liu received PhD with honors in computer science from Peking University, Beijing, China, in 2018. His research interests include image processing, computer vision, machine learning and medical image analysis. He was a recipient of the Best Student Paper Award at the 2017 IEEE Visual Communications and Image Processing, the 2019 Doctoral Dissertation Award of Beijing Society of Image and Graphics, and a co-recipient of the Best Paper Award at the 2019 MICCAI Workshop on Clinical Image-Based Procedures.
Publications
Journal Articles
1. Hangfan Liu, Hongming Li, Mohamad Habes, Yuemeng Li, Pamela Boimel, James Janopaul-Naylor, Ying Xiao, Edgar Ben-Josef and Yong Fan, “Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis”, IEEE Transactions on Biomedical Engineering (TBME), DOI: 10.1109/TBME.2020.2969839, 2020.
2. Hangfan Liu, Ruiqin Xiong, Xiaopeng Fan, Debin Zhao, Yongbing Zhang and Wen Gao, “CG-Cast: Scalable Wireless Image SoftCast Using Compressive Gradient”, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), vol. 29, no. 6, pp. 1832–1843, Jun. 2019.
3. Hangfan Liu, Ruiqin Xiong, Dong Liu, Siwei Ma, Feng Wu and Wen Gao, “Image Denoising via Low Rank Regularization Exploiting Intra and Inter Patch Correlation”, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), vol. 28, no. 12, pp. 3321–3332, Dec. 2018.
4. . Hangfan Liu, Ruiqin Xiong, Xinfeng Zhang, Yongbin Zhang, Siwei Ma and Wen Gao, “Non-Local Gradient Sparsity Regularization for Image Restoration”, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), vol. 27, no. 9, pp. 1909–1921, Sept. 2017.
5. Ruiqin Xiong, Hangfan Liu, Xinfeng Zhang, Jian Zhang, Siwei Ma, Feng Wu and Wen Gao, “Image Denoising via Bandwise Adaptive Modeling and Regularization Exploiting Nonlocal Similarity”, IEEE Transactions on Image Processing (TIP), vol. 25, no. 12, pp. 5793–5805, Dec. 2016.
6. Hangfan Liu, Ruiqin Xiong, Jing Zhao, Hongming Li, Siwei Ma and Wen Gao, “Image Gradient Based Wireless Soft Transmission”, Chinese Journal of Computers (CJC), vol. 42, no. 9, pp. 1905-1917, Sept. 2019.
7. Yongqin Zhang, Ruiwen Kang, Xianlin Peng, Jun Wang, Jihua Zhu, Jinye Peng, Hangfan Liu, “Image Denoising via Structure-Constrained Low-Rank Approximation”, Neural Computing and Applications (NCAA), DOI: 10.1007/s00521-020-04717-w, 2020.
Conference Papers
1. Hangfan Liu, Ruiqin Xiong, Jian Zhang and Wen Gao, “Image Denoising via Adaptive Soft-Thresholding Based on Non-Local Samples”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 484–492, Boston, USA, Jun. 2015.
2. Hangfan Liu, Hongming Li, Yuemeng Li, Shi Yin, Pamela Boimel, James Janopaul-Naylor, Haoyu Zhong, Ying Xiao, Edgar Ben-Josef and Yong Fan, “Adaptive Sparsity Regularization Based Collab-orative Clustering for Cancer Prognosis”, Medical Image Computing and Computer Assisted Inter-ventions (MICCAI), pp. 583–592, Shenzhen, China, Oct. 2019.
3. Hangfan Liu, Ruiqin Xiong, Qiang Song, Feng Wu and Wen Gao, “Image Super-Resolution Based on Adaptive Joint Distribution Modeling”, IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, USA, Dec. 2017. (Best Student Paper Award)
4. Hangfan Liu, Ruiqin Xiong, Xiaopeng Fan, Siwei Ma and Wen Gao, “Wireless Image SoftCast Using Compressive Gradient”, IEEE Data Compression Conference (DCC), pp. 451, Snowbird, Utah, USA, Apr. 2017.
5. Ruiqin Xiong, Hangfan Liu, Siwei Ma, Xiaopeng Fan, Feng Wu and Wen Gao, “G-CAST: Gradient Based Image SoftCast for Perception-Friendly Wireless Visual Communication”, IEEE Data Com-pression Conference (DCC), pp. 133–142, Snowbird, USA, Mar. 2014.
6. Hangfan Liu, Ruiqin Xiong, Siwei Ma, Xiaopeng Fan and Wen Gao, “Gradient Based Image Transmission and Reconstruction using Non-Local Gradient Sparsity Regularization”, IEEE Interna-tional Conference on Multimedia & Expo (ICME), Chengdu, China, Jul. 2014.
7. Hangfan Liu, Xinfeng Zhang and Ruiqin Xiong, “Content-Adaptive Low Rank Regularization for Image Denoising”, IEEE International Conference on Image Processing (ICIP), pp. 3091 – 3095, Phoenix, Arizona, USA, Sept. 2016.
8. Hangfan Liu, Ruiqin Xiong, Siwei Ma, Xiaopeng Fan and Wen Gao, “Non-Local Extension of Total Variation Regularization for Image Restoration”, IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1102–1105, Melbourne, Australia, Jun. 2014.
9. Hangfan Liu, Ruiqin Xiong, Siwei Ma, Xiaopeng Fan and Wen Gao, “Gradient Based Image/Video SoftCast with Grouped-Patch Collaborative Reconstruction”, IEEE Visual Communications and Image Processing (VCIP), pp. 141–144, Valletta, Malta, Dec. 2014.
10. Hangfan Liu, Ruiqin Xiong, Dong Liu, Feng Wu and Wen Gao, “Low Rank Regularization Ex-ploiting Intra and Inter Patch Correlation for Image Denoising”, IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, USA, Dec. 2017.
11. Hangfan Liu, Ruiqin Xiong, Xiaopeng Fan, Chong Luo and Wen Gao, “Compressive Gradient Based Scalable Image SoftCast”, IEEE Visual Communications and Image Processing (VCIP), St. Peters-burg, USA, Dec. 2017.
12. Hangfan Liu, Hongming Li, Pamela Boimel, James Janopaul-Naylor, Haoyu Zhong, Ying Xiao, Edgar Ben-Josef and Yong Fan, “Collaborative Clustering of Subjects and Radiomic Features for Predicting Clinical Outcomes of Rectal Cancer Patients”, IEEE International Symposium on Bio-medical Imaging (ISBI), pp. 1303–1306, Venice, Italy, Apr. 2019.
13. Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Hangfan Liu, Susan L. Furth, Katherine Fischer, Gregory E. Tasian, Yong Fan, “Multi-Instance Deep Learning with Graph Convo-lutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging”, MICCAI Workshop on Clinical Image-Based Procedures (CLIP), pp. 146-154, Shenzhen, China, Oct. 2019. (Best Paper Award)