个人简介
王珊珊，双博士，副研究员，博士生导师。2014年获信息技术与生物医学工程双博士学位，2018海外华人磁共振协会OCSMRM杰出研究奖（Outstanding Research Award）获得者，澳大利亚John Make peace Bennett最佳博士论文奖提名者，。 迄今为止发表英文学术论文70多篇，其中国际SCI期刊论文36篇，发明授权专利9项，两项转让国内龙头医疗企业联影医疗。发表的一作快速磁共振成像论文曾被领域内著名医学物理SCI期刊“Physics in Medicine and Biology”选为“Featured Article”及2016年度“Research Highlight”。快速医学成像获广东省科技发明一等奖。为国际医学磁共振年会2018-2020基于机器/深度学习磁共振成像与分析的分会主席，ISMRM/ISICDM/MIDL/MICCAI workshop session/area chair,曾4次获得ISMRME.KZavoisky奖金，为国际SCI期刊Biomedical Signal Processing and Control（SCI，JCR-2）的副主编, Magnetic resonance in medicine和IEEE reviews in Biomedical Engineering的编委；多个国际知名SCI期刊（如IEEETMI、IEEETIP、IEEETSP、IEEETIE、MRM，SignalProcessing等）及国际知名会议（如MICCAI,CVPR,ISBI）的受邀审稿人,曾获MRM和signal processing审稿杰出贡献奖。先后主持国家自然科学基金面上青年及省重点市级项目12项，曾受邀到世界顶级大学及会议如美国哈佛大学、加拿大魁北克大学、Gordon conference及ISMRM等给大会或教育讲座。
招生专业
081104-模式识别与智能系统
081002-信号与信息处理
083100-生物医学工程
招生方向
深度学习、快速成像与放射组学
计算机视觉，图像处理
机器学习
教育背景
2011-10--2014-06 悉尼大学 博士学位
2009-09--2014-06 上海交通大学 博士学位
2005-09--2009-07 中南大学 学士学位
教授课程
深度学习导论
科研项目
（ 1 ） 基于深度卷积神经网络的快速磁共振成像方法研究, 主持, 国家级, 2017-01--2019-12
（ 2 ） 基于协作结构稀疏性的多通道磁共振成像研究, 主持, 省级, 2016-09--2018-08
（ 3 ） 基于自适应稀疏表达的快速高分辨率磁共振成像研究, 主持, 省级, 2015-08--2018-08
（ 4 ） 基于字典学习的磁共振退化因素双校正快速成像技术研究, 主持, 省级, 2014-12--2016-12
（ 5 ） 基于字典学习的磁共振退化因素校正成像研究, 主持, 部委级, 2015-01--2016-12
（ 6 ） 磁共振成像与多模系统, 参与, 省级, 2015-01--2017-12
（ 7 ） PET-MRI成像理论与关键技术研究, 参与, 省级, 2015-01--2017-12
（ 8 ） 基于影像基因组学的乳腺癌精准临床诊断及预后评估模型开发, 主持, 省级, 2017-07--2020-06
（ 9 ） 基于深度先验学习的头颈一体化磁共振血管壁快速成像关键问题研究, 主持, 国家级, 2019-01--2021-12
（ 10 ） 脑卒中相关血管床粥样硬化斑块的快速磁共振成像及智能诊断研究, 参与, 国家级, 2019-01--2023-12
参与会议
（1） Investigation of convolutional neural network based deep learning for cardiac imaging 第26届国际磁共振大会 2018-06-14
（2）1D partial parallel MR imaging via deep convolutional neural network 第25届国际磁共振年会 2017-04-22
（3）Exploiting deep convolutional neural network for fast magnetic resonance imaging 第24届国际磁共振年会 2016-05-07
（4）Accelerating Magnetic Resonance Imaging Via Deep Learning 第13届国际生物医学成像研讨会 2016-04-13
（5）用于磁共振图像重建的字典学习 2015生物医学媒体计算国际学术研讨会 2015-09-22
（6）Parallel magnetic resonance imaging via dictionary learning 第23届国际磁共振年会 2015-05-30
（7）Parallel imaging via sparse representation over a learned dictionary 第12届国际生物医学成像研讨会 2015-04-16
近期论文
查看导师最新文章
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1. M Zhang, M Li, J Zhou, Y Zhu, Shanshan Wang, D Liang, Y Chen, Q Liu, High-dimensional Embedding Network Derived Prior for Compressive Sensing MRI Reconstruction, Medical image analysis, 2020, Code https://github.com/yqx7150/EDMSPRec.
2. Jinjie Zhou, Zhuonan He, Xiaodong Liu, Yuhao Wang, Shanshan Wang, Qiegen Liu, Transformed denoising autoencoder prior for image restoration, Journal of Visual Communication and Image Volume 72, October 2020, 102927 Code: https://github.com/yqx7150/TDAEP.
3. Shanshan Wang, Huitao Cheng, Leslie Ying, Taohui Xiao, Ziwen Ke, Hairong Zheng and Dong Liang, DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution, Magnetic resonance imaging, 2020, DOI: 10.1016/j.mri.2020.02.002 , Code: https://github.com/CedricChing/DeepMRI
4. Cheng Li, Jingxu Xu, Qiegen Liu, Yongjin Zhou, Lisha Mou, Zuhui Pu, Yong Xia, Hairong Zheng, and Shanshan Wang*, Multi-view mammographic density classification by dilated and attention-guided residual learning, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020, Code: https://github.com/lich0031/Mammographic_Density_Classification
5. Yongjin Zhou, Weijian Huang, Pei Dong, Yong Xia, and Shanshan Wang*, D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2019, DOI 10.1109/TCBB.2019.2939522, Code: https://github.com/SZUHvern/D-UNet
6. Hui Sun, Cheng Li, Boqiang Liu, Zaiyi Liu, Meiyun Wang, Hairong Zheng, David Dagan Feng and Shanshan Wang*, AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms, Physics in medicine and biology, 2019, code: https://github.com/lich0031/AUNet
7. Wei Zeng, Jie Peng, Shanshan Wang, Qiegen Liu, A Comparative Study of CNN-based Super-resolution Methods in MRI Reconstruction and Its Beyond, Signal processing: image communication, Volume 81, February 2020, 115701, code: https://github.com/yqx7150/DCCN.
8. Yiling Liu, Qiegen Liu, Minghui Zhang, Q. Yang, Shanshan Wang and Dong Liang, “IFR-Net: Iterative Feature Refinement Net-work for Compressed Sensing MRI,” IEEE Transactions on Computational Imaging. DOI: 10.1109/TCI.2019.2956877, Vol 434 – 446, 29 November 2019, https://github.com/yqx7150/IFR-Net-Code.
9. Qiegen Liu, Qingxin Yang, Huitao Cheng, Shanshan Wang, Minghui Zhang, Dong Liang, Highly undersampled magnetic resonance imaging reconstruction using autoencoder priors, Magnetic Resonance in Medicine, DOI: 10.1002/mrm.27921, 2019, https://github.com/yqx7150/EDAEPRec/blob/master/version2.
10. Shanshan Wang, Ziwen Ke, Huitao Cheng, Sen Jia, Leslie Ying, Hairong Zheng, Dong Liang. DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training, NMR in Biomedicine: 2019 , DOI:10.1002/nbm.4131, code: https://github.com/Keziwen/DIMENSION.
11. Minghui Zhang, Fengqin Zhang, Qiegen Liu, Shanshan Wang*, VST-Net: Variance-stabilizing Transformation Inspired Network for Poisson Denoising, Journal of visual communication and image representation, Volume 62, July 2019, Pages 12-22, Doi: https://doi.org/10.1016/j.jvcir.2019.04.011, Code: https://github.com/yqx7150/VST-Net.
12. Qiegen Liu, Shanshan Wang, Dong Liang, “Sparse and Dense Hybrid Representation via Subspace Modeling for Dynamic MRI”, Computerized Medical Imaging and Graphics. Volume 56, March 2017, Pages 24–37.（SCI, IF:1.385）Code: https://drive.google.com/drive/folders/0B3EiIvcKNZj8fkplX1JGR21yNjdORkhralp1NGxNb1RTRGFfOWZ0dGthNk5CeVpBV1FWZVE.
13. Qiegen Liu, Shanshan Wang, Leslie Ying, Xi Peng, Yanjie Zhu, and Dong Liang, “Adaptive Dictionary Learning in Sparse Gradient Domain for Image Recovery”, IEEE Transactions on Image Processing, 22 (2013), 4652-4663. (SCI, IF: 3.111), Accepted July 25, 2013. Date of publication August 15, 2013, Code https://drive.google.com/drive/folders/0B3EiIvcKNZj8UWZ5RUE4RHl5S00.
14. Qiegen Liu, Shanshan Wang, Kun Yang, Jianhua Luo, Yuemin Zhu, and Dong Liang, “Highly Undersampled Magnetic Resonance Image Reconstruction Using Two-Level Bregman Method with Dictionary Updating”, IEEE Transactions on Medical Imaging, 32 (2013), 1290-1301. (SCI, IF: 3.799) accepted March 25, 2013. Date of publication April 02, 2013, Code: https://drive.google.com/drive/folders/0B3EiIvcKNZj8cW4zZC1uSnJPUUUdrive/folders/0B3EiIvcKNZj8cW4zZC1uSnJPUUU.
15. Qiegen Liu, Shanshan Wang, Jianhua Luo, “A Novel Predual Dictionary Learning Algorithm,” Journal of Visual Communication and Image Representation, 23 (2012), pp. 182-193. (SCI, IF: 1.361) Accepted 19 September 2011, Available online 25 September 2011, https://github.com/yqx7150/yqx7150/PDL_ALM_DL_code.
16. Qiegen Liu, Jianhua Luo, Shanshan Wang, Moyan Xiao, and Meng Ye, “An Augmented Lagrangian Multi-Scale Dictionary Learning Algorithm,” EURASIP Journal on Advances in Signal Processing, vol. 2011, no. 1, pp. 1-16, 2011. (SCI, IF: 0.808) Accepted: 12 September 2011, Published: 12 September 2011,Code: https://github.com/yqx7150/PDL_ALM_DL_code.
17. Xiangshun Liu, Minghui Zhang, Qiegen Liu, Taohui Xiao, Hairong Zheng, Leslie Ying, Shanshan Wang*, Multi-Contrast MR Reconstruction with Enhanced Denoising Autoencoder Prior Learning, 17th International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Iowa City, Iowa, United States 2020 (EI). Code: https://github.com/yqx7150.
18. Yanxia Chen, Taohui Xiao, Cheng Li, Qiegen Liu and Shanshan Wang*, Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging. 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'19), Shenzhen, China, 2019.Code: https://github.com/yanxiachen/ConvDe-AliasingNet.
19. Kehan Qi, Hao Yang, Cheng Li, Zaiyi Liu, Meiyun Wang, Qiegen Liu and Shanshan Wang*, X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies, 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, 2019. (EI), Code: https://github.com/Andrewsher/X-Net.
20. Hao Yang, Weijian Huang, Kehan Qi, Cheng Li, Xinfeng Liu, Meiyun Wang, Hairong Zheng, and Shanshan Wang*, CLCI-Net: Cross-Level Fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke, 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'19), Shenzhen, China, 2019. Code: https://github.com/YH0517/CLCI_Net.
21. Yuan Yuan, Jinjie Zhou, Zhuonan He, Shanshan Wang, Biao Xiong, Qiegen Liu, High-dimensional embedding denoising autoencoding prior for color image restoration, 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019 (EI).Code: https://github.com/yqx7150/M2DAEP.
22. Wei Zeng, Jie Peng, Shanshan Wang, Zhicheng Li, Qiegen Liu, Dong Liang, “A comparative study of CNN-based super-resolution methods in MRI reconstruction", 16th International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Venice, Italy 2019 (EI). https://github.com/yqx7150/DCCN.
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