General

Shanshan Wang is currently an Associate Professor at Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS). She received her Dual-Ph.D. degree: Ph.D. in Computer Science, from the University of Sydney (USYD), and Ph.D. in Biomedical Engineering, from Shanghai Jiao Tong University (SJTU) respectively. Dr. Wang was a finalist for the Australian John Makepeace Bennett Best Thesis Award, and received the 2018 OCSMRM Outstanding Research Award and 2020 Wu Wenjun Artificial Intelligence Youth Award. Her research interests include machine learning, fast medical imaging and biomedical signal analysis. Dr. Wang has published over 90 peer-reviewed papers in these areas. Her initial fast-imaging research outcome was published in Physics in Medicine and Biology, and was selected as the “2016 Research Highlight” and “Featured Article”. She has been the moderator for machine learning for image reconstruction and processing scientific sessions of ISMRM from 2018-2020; the area chair for MIDL 2020; and invited to give a plenary talk in theGordon conference on in vivo MR”, to feature her pioneering work in accelerating MR imaging with deep learning. Dr. Wang is associate editor of “Biomedical Signal Processing and Control”, deputy editor for “Magnetic Resonance in Medicine” and editorial committee member for “IEEE reviews in biomedical engineering”, etc. She received the journal awards for “outstanding contribution in reviewing signal processing”, "distinguished reviewer for IEEE-TMI" and “distinguished service to MRM”. She is the principal investigator for oven 10 research projects in learning image reconstruction and analysis for medical imaging supported by different grant agencies such as NSFC and NSFG. She is IEEE Senior member, OCSMRM life/BoT member and ISMRM member.


Research Areas

Fast medical imaging; Deep learning; Artificial intelligence; Radiomics; 

Education

Sept   2009 - June 2014

Dual PhD in Information Technologies and Biomedical Engineering

Part   2 (Oct 2011 - June 2014)

PhD graduate in   Information Technologies, renamed as computer science

School   of IT, the University of Sydney, Sydney, NSW

Part   1 (Sept 2009 - Jun 2014)

PhD graduate in   Biomedical Engineering

School   of Life Science and Biotechnology, Shanghai Jiao Tong University

Sept   2005 - Jul 2009

Bachelor of Biomedical   Engineering (Top 3%)

Central   South University, Changsha, Hunan, P.R. China


Professional experiences

·           Deputy editor, “Magnetic resonance in medicine”

·           Associate Editor, "Biomedical Signal Processing and Control"

·           Editorial committee member, “IEEE reviews in biomedical engineering”

·           Editor, “International journal of medical imaging”

·           Guest associate editor, “Medical Physics”

·           Board member, ISMRM education committee.

·           Board member, ISMRM Web editorial committee.

·           Board member, OCSMRM Board of Trustee committee

·           Special issue editor for Journal of Health Engineering

·           Special issue editor for Sensing and Imaging

·           Scientific moderators for ISMRM AI imaging and analysis 2018-2020

·           Area chair, MIDL 2020-now

·           Session chair, MICCAI workshop 2020

·           Session chair, MICS 2020

·           Session chair, ISICDM 2019


Work Experience

Dec.   2016 – Now

Jun   2014 – Nov.2016

Associate   professor, Paul C. Lauterbur Research Center, SIAT, Chinese Academy of   Sciences

Assistant   professor, Paul C. Lauterbur Research Center, SIAT, Chinese Academy of   Sciences

Oct   2011 - Mar 2012

Research   assistant, the University of Sydney, Sydney, New South Wales, Australia


Teaching Experience

Deep learning induction, 40 hours 

Fundamentals of artificial intelligence- Introduction to deep learning, 40 hours

Artificial Intelligence in Medical Imaging

Publications

SCI -indexed Journal Papers:

1.          Weijian Huang, Hao Yang, Xinfeng Liu, Cheng Li, Ian Zhang, Rongpin Wang, Hairong Zheng, Shanshan Wang*, A coarse-to-fine framework for unsupervised multi-contrast MR image deformable registration with dual consistency constraint, IEEE Transactions on Medical Imaging, 2021, DOI:10.1109/TMI.2021.3059282. Code: https://github.com/SZUHvern/TMI_multi-contrast-registration.

2.          Shanshan Wang#*, Taohui Xiao#, Qiegen Liu, Hairong Zheng*, Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data, Biomedical Signal Processing and Control, 2021, 68:102579.

3.          Hongyu Wang, Shanshan Wang, Zibo Qin, Yanning Zhang, Ruijiang Li, Yong Xia, Triple Attention Learning for Classification of 14 Thoracic Diseases Using Chest Radiography, Medical Image Analysis, 2021, 67: 101846.

4.          Cong Quan, Jinjie Zhou, Yuanzheng Zhu, Yang Chen, Shanshan Wang, Dong Liang, Homotopic Gradients of Generative Density Priors for MR Image Reconstruction, IEEE Transactions on Medical Imaging, 2021. 

5.          Rui Yang, Yang Du, Xiaodong Weng, Zhiyuan Chen, Shanshan Wang, Xiuheng Liu, Automatic recognition of bladder tumours using deep learning technology and its clinical application, The International Journal of Medical Robotics and Computer Assisted Surgery, 2021, 17(2), e2194.

6.          Yu Gong, Hongming Shan, Yueyang Teng, Ning Tu, Ming Li, Guodong Liang, Ge Wang, Shanshan Wang. Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising, IEEE Transactions on Radiation and Plasma Medical Sciences, 2020.

7.          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.

8.        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

9.        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.

10.     Wenqing Hua#, Taohui Xiao#, Xiran Jiang, Zaiyi Liu, Meiyun Wang, Shanshan Wang*, Lymph-Vascular Space Invasion Prediction in Cervical Cancer: Exploring Radiomics and Deep Learning Multilevel Features of Tumor and Peritumor Tissue on Multiparametric MRI, Biomedical signal processing and control, 202058: 101869.

11.     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.

12.     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

13.     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.

14.     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.

15.     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

16.     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.

17.     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.

18.     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.

19.     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.

20.     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

21.     Biao Xiong, Qiegen Liu, Member, Jiaojiao Xiong, Sanqian Li, Shanshan Wang, Dong Liang, Field-of-Experts Filters Guided Tensor Completion, IEEE Trans. Multimedia. Volume 20, Issue 9, 2316-2329,2018

22.     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 SCI 3.942Date of Publication: 29 August 2017

23.     Yong Xia, Weidong Cai, X Yang, Shanshan Wang,Computation Methods for Biomedical Information Analysis, Journal of Healthcare Engineering, Editorial,  https://doi.org/10.1155/2018/8683601, 2018

24.     Jing Cheng,  Sen Jia, Leslie Ying, Yuanyuan Liu, Shanshan Wang, Yanjie Zhu,Ye Li, Chao Zou, Xin Liu, Dong Liang, Improved Parallel Image Reconstruction via Feature Refinement, Magnetic Resonance in Medicine, 2017, DOI:10.1002/mrm.27024SCI, 3.924

25.     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:1.385Code: https://drive.google.com/drive/folders/0B3EiIvcKNZj8fkplX1JGR21yNjdORkhralp1NGxNb1RTRGFfOWZ0dGthNk5CeVpBV1FWZVE.

26.     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. 32913316, 2016. (SCI, 2.761). (Accepted for publication 4 February 2016, Published 1 April 2016)

27.     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

28.     Shanshan Wang, Qiegen Liu, Yong Xia, Pei Dong, Jianhua Luo, Qiu Huang and David Dagan Feng. “Dictionary Learning Based Impulse Noise Removal via L1-L1 Minimization”, Signal Processing, 93 (2013), 2696-2708. (SCI, IF: 2.238)  Accepted5 March2013, Available online 13March2013

29.     Pei Dong#, Shanshan Wang#*, Yong Xia, Dong Liang, David Dagan Feng, “Foreground Detection with Simultaneous Dictionary Learning and Historical Pixel Maintenance”, IEEE Transactions on Image Processing, Vol 25, Issue:11, p5035-5049, (# means co-first author and *means corresponding author) (SCI, IF: 3.111) accepted July 21, 2016. Date of publication August 10,2016)共同一作兼通讯

30.     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.

31.     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.

32.     Shanshan Wang, Yong Xia, Qiegen Liu, Jianhua Luo, Yuemin Zhu, and David Dagan Feng. “Gabor Feature Based Nonlocal Means Filter for Textured Image Denoising,” Journal of Visual Communication and Image Representation, 23 (2012), pp. 1008-1018. (SCI, IF: 1.361) Accepted 4 June 2012 Available online 29 June 2012

33.     Shanshan Wang, Jianbo Liu, Xi Peng, Pei Dong, Qiegen Liu and Dong Liang, Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging, BioMed Research International, vol. 2016, Article ID 2860643, 7 pages, 2016. doi:10.1155/2016/2860643. (SCI, IF: 2.134)

34.     Shanshan Wang, Yong Xia, Pei Dong, Jianhua Luo, Qiu Huang, David Dagan Feng, and Yuanxiang Li, “Bias Correction for Magnetic Resonance Image via Joint Entropy Regularization”, Bio-Medical Materials and Engineering, 24 (2014), 1239-1245. (SCI, IF: 0.847) Accepted: 1 March 2014

35.     Jianhua Luo, Shanshan Wang, Wanqing Li, and Yuemin Zhu, “Removal of Truncation Artefacts in Magnetic Resonance Images by Recovering Missing Spectral Data,” Journal of Magnetic Resonance, 224 (2012), 82-93. (SCI, IF: 2.315)  Accepted 4 June 2012, Available online 29 June 2012

36.     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.

37.     Qiegen Liu, Shanshan Wang, Jianhua Luo, Yuemin Zhu, and Meng Ye, “An Augmented Lagrangian Approach to General Dictionary Learning for Image Denoising,” Journal of Visual Communication and Image Representation, vol. 23, no. 5, pp. 753-766, 2012. (SCI, IF: 1.361) Accepted 5 April 2012, Available online 14 April 2012

38.     Jianbo Liu#, Shanshan Wang#, Xi Peng and Dong Liang, "Undersampled MR Image Reconstruction With Data-Driven Tight Frame", Computational and Mathematical Methods in Medicine, (# indicates co-first authors), 2015 Volume 2015 (2015), Article ID 424087, 10 pages, http://dx.doi.org/10.1155/2015/424087, (SCI, 0.766) Accepted 10 June 2015

39.     Pei Dong, Yong Xia, Shanshan Wang, Li Zhuo, and David Dagan Feng, “An Iteratively Reweighting Algorithm for Dynamic Video Summarization,” Multimedia Tools and Applications, November 2015, Volume 74, Issue 21, pp 9449-9473 (SCI, IF 1.058). Accepted: 26 May 2014

40.     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.

41.     Nian Cai, Weisi Xie, Zhenghang Su, Shanshan Wang, and Dong Liang, Sparse parallel MRI based on accelerated operator splitting schemes, Computational and Mathematical Methods in Medicine, vol. 2016, Article ID 1724630, 14 pages, 2016. doi:10.1155/2016/1724630.

42.     Xinfeng Liu, Hao Yang, Kehan Qi, Pei Dong, Qiegen Liu, Xin Liu, Rongpin Wang* and Shanshan Wang*, MSDF-Net: Multi-Scale Deep Fusion Network for Stroke Lesion Segmentation, IEEE Access , DOI” 10.1109/ACCESS.2017.DOI

43.     Minghui  Zhang, Yuan Yuan, Fengqin Zhang, Siyuan Wang, Shanshan Wang* and Qiegen Liu*, "Multi-Noise and Multi-Channel Derived Prior Information for Grayscale Image Restoration," in IEEE Access, vol. 7, pp. 150082-150092, 2019. doi: 10.1109/ACCESS.2019.2946994

Conference Papers:

44.       Li, Cheng, Jin Ye, Junjun He, Shanshan Wang, Lixu Gu, Yu Qiao, Collaborative Multi-View Convolutions With Gating For Accurate And Fast Volumetric Medical Image Segmentation, IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.

45.       Cheng Li, Hui Sun, Taohui Xiao, Xin Liu, Hairong Zheng, and Shanshan Wang, A novel unsupervised domain adaptation method for deep learning-based prostate MRimage segmentation, Proc. 29th Annual Meeting of ISMRM, Vancouver, Canada, 2021.

46.       Kehan Qi, Yu Gong, Haoyun Liang, Xin Liu, Hairong Zheng, and Shanshan Wang, Multi-task Imaging for Deep Learning, Proc. 29th Annual Meeting of ISMRM, Vancouver, Canada, 2021.

47.       Weijian Huang, Yulon Qi, Qiang He, Ting Ma, Xin Liu, Guanxun Cheng, Hairong Zheng, and Shanshan Wang, Stroke analysis with fully automatic multi-contrast MR image registration, Proc. 29th Annual Meeting of ISMRM, Vancouver, Canada, 2021.

48.       Yu Zhang, Weijian Huang, Fei Li, Qiang He, Haoyun Liang, Xin Liu, Hairong Zheng, and Shanshan Wang, Unsupervised deep learning for multi-modal MR image registration with topology-preserving dual consistency constraint, Proc. 29th Annual Meeting of ISMRM, Vancouver, Canada, 2021.

49.       Haoyun Liang, Yu Gong, Hoel Kervadec, Cheng Li, Jing Yuan, Xin Liu, Hairong Zheng, Shanshan Wang*, Laplacian pyramid-based complex neural network learning for fast MR imaging, Medical Imaging with Deep Learning (MIDL), Montreal, Canada, 2020

50.       Cheng Li, Hui Sun, Taohui Xiao, Zaiyi Liu, Qiegen Liu, Xin Liu, Hairong Zheng, and Shanshan WangBreast lesion segmentation in MR images through knowledge distillation-based modality speculation, Proc. 28th Annual Meeting of ISMRM, Sydney, Australia, 2020,

51.       Taohui Xiao, Cheng Li, Haoyun Liang, Hairong Zheng, and Shanshan Wang, Are deep learning MR reconstruction models robust against adversarial attacks? Proc. 28th Annual Meeting of ISMRM, Sydney, Australia, 2020

52.       Hao Yang, Kehan Qi, Xin Yu, Hairong Zheng, Shanshan Wang, Multi-scale Entity Encoder-decoder Network Learning for Stroke Lesion Segmentation, Proc. 28th Annual Meeting of ISMRM, Sydney, Australia, 2020

53.       Jiaxin Li, Cheng Li, Xiran Jiang, Chaohe Zhang, Zhenkun Peng, Qiegen Liu, and Shanshan Wang, A two-stage deep learning method for the identification of rectal cancer lesions in MR images, Proc. 28th Annual Meeting of ISMRM, Sydney, Australia, 2020

54.       Xiangshun Liu, Minghui Zhang, Qiegen Liu, Leslie Ying, Xin Liu, Hairong Zheng, and Shanshan Wang, Multi-contrast MR imaging with enhanced denoising autoencoder prior network learning, Proc. 28th Annual Meeting of ISMRM, Sydney, Australia, 2020.

55.       Haoyun Liang , Taohui Xiao , Chuyu Rong , Yu Gong , Cheng Li , and Shanshan Wang, Weakly Supervised Deep Prior Learning for Multi-coil MRI ReconstructionProc. 28th Annual Meeting of ISMRM, Sydney, Australia, 2020.

56.       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.

57.       Cheng Li, Jin Ye, Junjun He, Shanshan Wang, Yu Qiao*, Lixu Gu, Dense correlation network for automatic ocular multi-disease detection with paired color fundus photographs, 17th International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Iowa City, Iowa, United States 2020 (EI).

58.       Junjun He, Cheng Li, Jin Ye, Shanshan Wang, Yu Qiao, Lixu Gu*, Classification of ocular diseases employing attention-based unilateral and bilateral feature weighting and fusion, 17th International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Iowa City, Iowa, United States 2020(EI).

59.       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.

60.       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.

61.       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.

62.       Cheng Li, Hui Sun, Zaiyi Liu, Meiyun Wang, Hairong Zheng and Shanshan Wang*, Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation, 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, 2019.

63.       Taohui Xiao, Wenqing Hua, Cheng Li, Shanshan Wang*, Glioma Grading Prediction by Exploring Radiomics and Deep Learning Features, The 3rd International Symposium on Image Computing and Digital Medicine (ISICDM 2019), Xi'an, China, 2019.(EI)

64.       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.

65.       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

66.       Guanyu Li, Yiling Liu, Minghui Zhang, Shanshan Wang, Qiegen Liu and Dong Liang, A Network-Driven Prior Induced Bregman Model for Parallel MR Imaging, 43th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'19), Berlin, German, 2019. (EI).

67.        Shanshan Wang, Ziwen Ke , Huitao Cheng , Leslie Ying , Xin Liu , Hairong Zheng , and Dong Liang, Edge enhanced loss constraint for deep learning based dynamic MR imaging, Proc. 27th Annual Meeting of ISMRM, Montreal, QC, Canada, 2019.

68.        Shanshan Wang , Yanxia Chen , Leslie Ying , Cheng Li , Ziwen Ke , Taohui Xiao , Xin Liu , Dong Liang , and Hairong Zheng, DCTV-Net: Convolutional Neural Network for dynamic MRI, Proc. 27th Annual Meeting of ISMRM, Montreal, QC, Canada, 2019.

69.       Cheng Li, Hui Sun, Zaiyi Liu, Meiyun Wang, Hairong Zheng, and Shanshan WangAutomatic breast lesion segmentation in MR images employing a dense attention fully convolutional network, Proc. 27th Annual Meeting of ISMRM, Montreal, QC, Canada, 2019

70.       Ziwen Ke, Shanshan Wang, Huitao Cheng, Leslie Ying, Xin liu, Hairong Zheng, Dong Liang,Multi-supervised learning in cross-domain networks for cardiac imaging, Proc. 27th Annual Meeting of ISMRM, Montreal, QC, Canada, 2019.

71.       Ziwen Ke, Shanshan Wang, Cheng Li, Huitao Cheng, Leslie Ying, Xin liu, Hairong Zheng, Dong Liang, A Cascaded Residual Dense Network for Cardiac MR Imaging, Proc. 27th Annual Meeting of ISMRM, Montreal, QC, Canada, 2019.

72.       Dou Li, Shanshan Wang, Zemin Cai, Dong Liang, Jianhua Luo .Image reconstruction in CT from limited-angle projections. Proceedings of SPIE - The International Society for Optical Engineering .2019

73.       Shanshan Wang, Ziwen Ke, Huitao Cheng, Leslie Ying, Xin liu, Hairong Zheng, Dong Liang, Investigation of convolutional neural network based deep learning for cardiac imaging, Proc. 26th Annual Meeting of ISMRM, Paris, France, 2018.

74.       Shanshan Wang, Huitao Cheng, Ziwen Ke, Leslie Ying, Xin liu, Hairong Zheng, Dong Liang, Complex-valued residual network learning for parallel MR imaging, Proc. 26th Annual Meeting of ISMRM, Paris, France, 2018.

75.       Shanshan Wang, Tao Zhao, Ningbo Huang, Sha Tan, Yuanyuan Liu, Leslie Ying, and Dong Liang. “Feasibility of Multi-contrast MR imaging via deep learning”, 25th ISMRM annual meeting, Honolulu, United States Of America, 2017

76.       Shanshan Wang, Ningbo Huang, Tao Zhao, Yong Yang, Leslie Ying, and Dong Liang. “1D Partial Fourier Parallel MR imaging with deep convolutional neural network”, 25th ISMRM annual meeting, Honolulu, United States Of America, 2017

77.       Shanshan Wang, Taohui Xiao, Sha Tan, Yuanyuan Liu, Leslie Ying, and Dong Liang. “Undersampling trajectory design for fast MRI with super-resolution convolutional neural network”, 25th ISMRM annual meeting, Honolulu, United States Of America, 2017

78.       Shanshan Wang, Tao Zhao, Taohui Xiao, Sha Tan, Leslie Ying, Yangzhou Gan and Dong Liang , Accelerating multi-contrast MR imaging with deep learning exploring their intra-correlations, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'15), Jeju Island, Korea, 2017.Abstract.

79.       Shanshan Wang, Zhenghang Su, Leslie Ying, Xi Peng, Shun Zhu, Feng Liang, Dagan Feng, Dong Liang Title: "Accelerating Magnetic Resonance Imaging Via Deep Learning", 13th International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Prague, Czech Republic, 2016 (EI).

80.       Shanshan Wang, Leslie Ying, Xi Peng, Zhenghang Su and Dong Liang, "Exploiting deep convolutional neural network for fast magnetic resonance imaging", 24th ISMRM Annual Meeting, Singapore, 2016.

81.       Xi Peng, Shanshan Wang, Qingyong Zhu, Dong Liang, "Reference-guided CS-MRI with Gradient Orientation Priors", 24th ISMRM annual meeting, Singapore, 2016

82.       Jing Cheng, Leslie Ying, Shanshan Wang, Xi Peng, Yuanyuan Liu, Jing Yuan, Dong Liang, "Parallel Imaging Reconstruction from Undersampled K-space Data via Iterative Feature Refinement", 24th ISMRM annual meeting, Singapore, 2016.

83.       Shanshan Wang, Xi Peng, Pei Dong, Leslie Ying, David Dagan Feng, Dong Liang, Parallel imaging via sparse representation over a learned dictionary, 12th International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), New York, USA, 2015 (EI).

84.       Shanshan Wang,, X. Peng, J. Liu, Y. Liu, P., Dong and D. Liang, "Parallel magnetic resonance imaging via dictionary learning", 23th ISMRM Annual Meeting, Toronto, Canada, 2015, p2421.

85.       Xi Peng, Qingyong Zhu, Shanshan Wang, and Dong Liang. "Reference Guided CS-MRI with Gradient Orientation Priors", n Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’15), Milano, Italy, 2015 (EI).

86.       Xi Peng , Shanshan Wang, Y. Liu, D. Liang, “Model-based diffusion tensor denoising with tensor and FA smoothness constraints”, 23th ISMRM Annual Meeting, Toronto, Canada, 2015, p2811.

87.       Shanshan Wang, Yong Xia, Pei Dong, David Dagan Feng, Jianhua Luo, Qiu Huang. “Magnetic Resonance Image Restoration via Dictionary Learning under Spatially Adaptive Constraints”, in Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’13), Osaka, Japan, 2013 (EI)

88.       Shanshan Wang, Qiegen Liu, Jianhua Luo, Yueming Zhu. “A Novel Hybrid Compressed Sensing Image Reconstruction Method” in the proceedings of 2011 International Conference on Electronics and Optoelectronics (ICEOE), Dalian, China, 2011. (EI).

89.       Qiegen Liu, Gang Yang, Jianhua Luo and Shanshan Wang, “An Efficient Augmented Lagrangian Method for Impulse Noise Removal via Learned Dictionary”, in Proceedings of the International Conference on Computer, Informatics, Cybernetics and Applications, Hangzhou, China, 2011 (EI).

90.       Jianhua Luo, Shanshan Wang, Moyan Xiao, Lu Zhang. “Magnetic Resonance Image Denoising Using Spectral Data Substitution”, in Proceedings of the 3rd IEEE International Conference on BioMedical Engineering and Informatics, Yantai, China, 2010 (EI).


Patents

1.          Shanshan Wang, Zhenghang Su, Dong Liang, Jianbo Liu, Xin Liu and Hairong Zheng, “Dictionary learning based parallel magnetic resonance imaging method and device”, Application No. 201410614439. 3, Chinese Patent. Granted Number: ZL201410614439. 3Granted date2017-02-15

2.          Shanshan Wang, Dong Liang, Jianbo Liu, Zhenghang Su, Xin Liu and Hairong Zheng, “Self-adaptive parallel magnetic resonance imaging method and device”, Application No. 201410614836.0, Chinese Patent. Granted Number: ZL201410614836.0Granted date2017-06-09

3.          Dong Liang, Shanshan Wang, Sha Tan, Zhenghang Su, Xin Liu and Hairong Zheng, “Magnetic resonance imaging via deep convolutional neural network”, Application No. 201580001261.8, Chinese Patent (Granted Date: 2019-6-14)

4.          Dong Liang, Jianbo Liu, Shanshan Wang, Xin Liu and Hairong Zheng, “A fast Magnetic resonance imaging method via iterative feature refining”, Application No. 201410452350.1, Chinse Patent. Granted numberZL201410614836.0 Granted date2017-06-09

5.          Xi Peng, Dong Liang, Shanshan Wang, Yishuo An, Xin Liu and Hairong Zheng, “An efficient MR imaging method and system”, Application No. 201410849728.1, Chinese Patent, Granted numberZL201410849728.1 Granted date2017-04-05

6.          Dong Liang, Shanshan Wang, Sha Tan, Zhenghang Su, Xin Liu and Hairong Zheng, “Magnetic resonance imaging via deep convolutional neural network”, Application No. PCT/CN2015/099918, PCT International Patent

7.          Shanshan Wang, Dong Liang, Sha Tan, Jianbo Liu, Qiegen Liu, Xi Peng, Xin Liu, Hairong Zheng, A new MR imaging method and system, Application NoCN201611006925.2Application date2016-11-16

8.          Dong Liang, Shanshan Wang, Taohui Xiao, Xin Liu, Hairong Zheng, “A novel MR imaging method and system”, Application No. CN201710345402.9Application date2017-05-16

9.          Shanshan Wang, Dong Liang, Ningbo Huang, Xin Liu, Hairong Zheng, “Deep learning based 1D Partial Fourier Imaging” Patent numberPCT/CN2017/087233Application date2017-06-06

10.       Shanshan Wang, Dong Liang, Ningbo Huang, Xin Liu, Hairong Zheng, “Deep learning based 1D Partial Fourier Imaing” Patent numberCN201710416357.1Application date2017-06-06

11.       Dong Liang,Shanshan Wang, Hairong Zheng, Xin Liu, Sha Tan, “Deep learning based automatic MR scanning and system” Patent numberCN201710416357.1Application date2017-06-14

12.       Dong Liang, Shanshan Wang, Tao Zhao, Xin Liu, Hairong Zheng , “A novel MR imaging method and system”, Patent numberCN2017102363304 (Application date2017-04-11)


Research Interests

Deep/Machine Learning

Pattern Recognition

Radiomics

Fast medical imaging

Image processing


Codes and Softwares

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:1.385Code: 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.

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.