Shanshan Wang
Shanshan Wang
Professor of Paul C Lauterbur Research Center, Chinese Academy of Sciences
Потвърден имейл адрес: siat.ac.cn - Начална страница
Accelerating magnetic resonance imaging via deep learning
S Wang, Z Su, L Ying, X Peng, S Zhu, F Liang, D Feng, D Liang
2016 IEEE 13th international symposium on biomedical imaging (ISBI), 514-517, 2016
DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
S Wang, H Cheng, L Ying, T Xiao, Z Ke, H Zheng, D Liang
Magnetic Resonance Imaging 68, 136-147, 2020
D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation
Y Zhou, W Huang, P Dong, Y Xia, S Wang
IEEE/ACM transactions on computational biology and bioinformatics 18 (3 …, 2019
A radiomics approach with CNN for shear-wave elastography breast tumor classification
Y Zhou, J Xu, Q Liu, C Li, Z Liu, M Wang, H Zheng, S Wang
IEEE Transactions on Biomedical Engineering 65 (9), 1935-1942, 2018
X-net: Brain stroke lesion segmentation based on depthwise separable convolution and long-range dependencies
K Qi, H Yang, C Li, Z Liu, M Wang, Q Liu, S Wang
Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019
Adaptive dictionary learning in sparse gradient domain for image recovery
Q Liu, S Wang, L Ying, X Peng, Y Zhu, D Liang
IEEE Transactions on Image Processing 22 (12), 4652-4663, 2013
AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
H Sun, C Li, B Liu, S Wang
arXiv preprint arXiv:1810.10151, 2018
Highly undersampled magnetic resonance image reconstruction using two-level Bregman method with dictionary updating
Q Liu, S Wang, K Yang, J Luo, Y Zhu, D Liang
IEEE Transactions on Medical Imaging 32 (7), 1290-1301, 2013
DIMENSION: dynamic MR imaging with both k‐space and spatial prior knowledge obtained via multi‐supervised network training
S Wang, Z Ke, H Cheng, S Jia, L Ying, H Zheng, D Liang
NMR in Biomedicine 35 (4), e4131, 2022
Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision
H Kervadec, J Dolz, S Wang, E Granger, IB Ayed
Medical imaging with deep learning, 365-381, 2020
Triple attention learning for classification of 14 thoracic diseases using chest radiography
H Wang, S Wang, Z Qin, Y Zhang, R Li, Y Xia
Medical Image Analysis 67, 101846, 2021
Learning joint-sparse codes for calibration-free parallel MR imaging
S Wang, S Tan, Y Gao, Q Liu, L Ying, T Xiao, Y Liu, X Liu, H Zheng, ...
IEEE transactions on medical imaging 37 (1), 251-261, 2017
Dictionary learning based impulse noise removal via L1–L1 minimization
S Wang, Q Liu, Y Xia, P Dong, J Luo, Q Huang, DD Feng
Signal Processing 93 (9), 2696-2708, 2013
CLCI-Net: Cross-level fusion and context inference networks for lesion segmentation of chronic stroke
H Yang, W Huang, K Qi, C Li, X Liu, M Wang, H Zheng, S Wang
Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019
Invasive placenta previa: placental bulge with distorted uterine outline and uterine serosal hypervascularity at 1.5 T MRI–useful features for differentiating placenta percreta …
X Chen, R Shan, L Zhao, Q Song, C Zuo, X Zhang, S Wang, H Shi, F Gao, ...
European Radiology 28, 708-717, 2018
Annotation-efficient deep learning for automatic medical image segmentation
S Wang, C Li, R Wang, Z Liu, M Wang, H Tan, Y Wu, X Liu, H Sun, R Yang, ...
Nature communications 12 (1), 5915, 2021
IFR-Net: Iterative feature refinement network for compressed sensing MRI
Y Liu, Q Liu, M Zhang, Q Yang, S Wang, D Liang
IEEE Transactions on Computational Imaging 6, 434-446, 2019
Gabor feature based nonlocal means filter for textured image denoising
S Wang, Y Xia, Q Liu, J Luo, Y Zhu, DD Feng
Journal of Visual Communication and Image Representation 23 (7), 1008-1018, 2012
Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors
Q Liu, Q Yang, H Cheng, S Wang, M Zhang, D Liang
Magnetic resonance in medicine 83 (1), 322-336, 2020
Multi-view mammographic density classification by dilated and attention-guided residual learning
C Li, J Xu, Q Liu, Y Zhou, L Mou, Z Pu, Y Xia, H Zheng, S Wang
IEEE/ACM transactions on computational biology and bioinformatics 18 (3 …, 2020
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