
王珊珊 女 博导 中国科学院深圳先进技术研究院
电子邮件: ss.wang@siat.ac.cn
通信地址: 广东省深圳市南山区学苑大道1068号
邮政编码: 518055
研究领域
王珊珊 研究员,双博士,博士生导师,国家优秀青年基金获得者, 入选2022年度斯坦福发布“全球前2%顶尖科学家”榜单,广东省医学影像智能分析与应用重点实验室副主任。研究方向机器学习、快速医学成像、放射组学等, 迄今为止在Nature子刊、IEEE Trans TMI/TBME/TIP、MICCAI等顶级期刊与会议发表英文学术论文100多篇,ESI 高被引4篇,PMB年度亮点文章一篇,发明中国授权专利9项以及美国授权专利2项,4项实现规模化产业应用。曾获2018国际华人医学磁共振协会杰出研究奖,2020吴文俊人工智能优秀青年奖,2020广东省科技进步一等奖(排名2/10),2021年深圳市青年科技奖, IEEE Senior member, OCSMRM BoT/Life member, Gordon Plenary Lecturer,ISMRM NIBIB New Horizons Plenary Lecturer. 为四个国际SCI学术期刊的编委(含Magnetic resonance in medicine, Pattern recognition, IEEE reviews in biomedical engineering 和Biomedical signal processing and control)。
研究方向:人工智能(机器学习)、快速医学成像、图像处理、计算机视觉、放射组学
常年招收:博士生、博士后、研究助理、助理研究员等, 联系方式:ss.wang@siat.ac.cn
招生信息
专业背景:生物医学工程、电子工程、计算机、信号处理、数学等相关专业。对医学成像、机器学习、图像处理,计算机视觉感兴趣
研究方向:字典学习、深度学习、医学成像、计算机视觉、图像处理图像检测与分割 、医学影像分析
招生专业
招生方向
教授课程
学术奖励
(1) 广东省科技进步一等奖, 一等奖, 省级, 2020
(2) 吴文俊人工智能优秀青年奖, 部委级, 2020
(3) 海外华人磁共振杰出研究奖, OCSMRM, 2018
(4) 广东省技术发明一等奖, 一等奖, 省级, 2018
(5)深圳市青年科技奖,市级,2021
专利成果
科研活动
科研项目
参与会议
开源代码与程序
- Shanshan Wang, Cheng Li, Rongpin Wang, Zaiyi Liu, Meiyun Wang, Hongna Tan, Yaping Wu, Xinfeng Liu, Hui Sun, Rui Yang, Xin Liu, Jie Chen, Huihui Zhou, Ismail Ben Ayed, Hairong Zheng,Annotation-efficient deep learning for automatic medical image segmentation,Nature communications 12 (1), 1-13, https://zenodo.org/record/5511736#.YYzt78h6RWY
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.