研究员
| 职 称： 研究员 |
| 学 历： 博士 |
| 邮 箱： ss.wang@siat.ac.cn |
| 研究领域：人工智能快速医学成像、机器学习及影像组学 |
王珊珊，研究员，双博士，博士生导师，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 communications、IEEE Trans TMI/TBME/TIP、MICCAI等顶级期刊与会议发表英文学术论文50多篇，ESI 高被引2篇，PMB年度亮点文章1篇，发明授权专利9项，4项实现规模化产业应用。快速医学成像获广东省科技发明一等奖，影像组学定量分析工作获广东省科技进步一等奖。并获2018海外华人磁共振协会OCSMRM杰出研究奖（Outstanding Research Award），2020吴文俊人工智能优秀青年奖，2021年深圳市青年科技奖，IEEE Senior member,OCSMRM BoT/Life member, Gordon Plenary Lecturer， NIBIB New Horizons Lecturer。为 ISMRM/MICCAI/MIDL/ISICDM session/area chair。为多个国际SCI期刊Magnetic resonance in medicine，Pattern recognition和 Biomedical Signal Processing and Control等的编委(DE/AE/EBM)。
1.国自然面上项目 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 项目负责人
2.国自然青年科学基金项目 基于深度卷积神经网络的快速磁共振成像方法研究 项目负责人
3.国自然重点项目 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 子项目负责人
4.广东省省级科技计划项目 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 子项目负责人
5.深圳市基础研究（学科布局）项目 基20180248人工智能头颈斑块自动检测研究 项目负责人
1. Shanshan Wang, Yong Xia, Qiegen Liu, Pei Dong, and David Dagan Feng. “Fenchel Duality Based Dictionary Learning for Restoration of Noisy Images”, IEEE Transactions on Image Processing, 22 (2013), 5214-5225. (SCI, IF: 3.111). Accepted September 1, 2013. Date of publication September 20, 2013;
2. Shanshan Wang, Sha Tan, Yuan Gao, Qiegen Liu, Leslie Ying, Taohui Xiao, Yuanyuan Liu, Xin Liu, Hairong Zheng, and Dong Liang, Learning joint-sparse codes for calibration-free parallel MR imaging, IEEE Transactions Medical Imaging, 37(1):251-261, 2018
3. Shanshan Wang, Jianbo Liu, Qiegen Liu, Leslie Ying, Xin Liu, Hairong Zheng and Dong Liang, "Iterative feature refinement for accurate undersampled MR image reconstruction", Physics in Medicine and Biology, vol. 61, p. 3291－3316, 2016. (Research highlight, 年度亮点文章)
4. 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
5. 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
6. Yongjin Zhou , Jingxu Xu, Qiegen Liu, Cheng Li, Zaiyi Liu, Meiyun Wang, Hairong Zheng, Shanshan Wang*, A Radiomics Approach with CNN for Shear-wave Elastography Breast Tumor Classification, IEEE Transactions on Biomedical Engineering. 1935-1942, Volume 65, Issue 9, 2018
7. 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
8. 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
9. Xiran Jiang†, Jiaxin Li†,Yangyang Kan, Tao Yu, Shijie Chang, Xianzheng Sha, Hairong Zheng, Yahong Luo* and Shanshan Wang*, MRI Based Radiomics Approach with Deep Learning for Prediction of Vessel Invasion in Early-Stage Cervical Cancer, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2019, DOI: 10.1109/TCBB.2019.2963867