| 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 | 741 | 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 | 125 | 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 | 112 | 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 | 107 | 2018 |
| 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 | 106 | 2013 |
| 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 | 103 | 2019 |
| 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 | 83 | 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 | 81 | 2022 |
| 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 | 80 | 2018 |
| 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 | 69 | 2020 |
| 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 | 65 | 2017 |
| 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 | 61 | 2021 |
| 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 | 59 | 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 | 51 | 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 | 47 | 2012 |
| 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 | 45 | 2019 |
| 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 | 41 | 2020 |
| 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 | 39 | 2021 |
| 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 | 36 | 2020 |
| Lymph-vascular space invasion prediction in cervical cancer: exploring radiomics and deep learning multilevel features of tumor and peritumor tissue on multiparametric MRI W Hua, T Xiao, Z Liu, M Wang, H Zheng, S Wang Biomedical Signal Processing and Control 58, 101869, 2020 | 35 | 2020 |
Qiegen LiuNanchang universityDirección de correo verificada de ncu.edu.cn
