1 code implementation • 17 Apr 2021 • Zhen Wang, Shan-Shan Wang, Lan Bai, Wen-Si Wang, Yuan-Hai Shao
In semi-supervised fuzzy clustering, this paper extends the traditional pairwise constraint (i. e., must-link or cannot-link) to fuzzy pairwise constraint.
5 code implementations • 14 Aug 2020 • Cong Quan, Jinjie Zhou, Yuanzheng Zhu, Yang Chen, Shan-Shan Wang, Dong Liang, Qiegen Liu
Deep learning, particularly the generative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently.
no code implementations • 5 Aug 2020 • Weijian Huang, Hao Yang, Xinfeng Liu, Cheng Li, Ian Zhang, Rongpin Wang, Hairong Zheng, Shan-Shan Wang
Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning.
no code implementations • 30 May 2020 • Shan-Shan Wang, Lei Zhang
In this way, the high accurate pseudolabeled target samples and semantic alignment can be captured simultaneously in the co-training process.
1 code implementation • MIDL 2019 • Hoel Kervadec, Jose Dolz, Shan-Shan Wang, Eric Granger, Ismail Ben Ayed
Particularly, we leverage a classical tightness prior to a deep learning setting via imposing a set of constraints on the network outputs.
1 code implementation • 13 Oct 2019 • Yu Gong, Hongming Shan, Yueyang Teng, Ning Tu, Ming Li, Guodong Liang, Ge Wang, Shan-Shan Wang
The contributions of this paper are twofold: i) a PT-WGAN framework is designed to denoise low-dose PET images without compromising structural details, and ii) a task-specific initialization based on transfer learning is developed to train PT-WGAN using trainable parameters transferred from a pretrained model, which significantly improves the training efficiency of PT-WGAN.
2 code implementations • 24 Sep 2019 • Yiling Liu, Qiegen Liu, Minghui Zhang, Qingxin Yang, Shan-Shan Wang, Dong Liang
To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (IFR-CS), equipped with fixed transforms, to restore the meaningful structures and details.
no code implementations • 24 Aug 2019 • Shan-Shan Wang, Yanxia Chen, Taohui Xiao, Ziwen Ke, Qiegen Liu, Hairong Zheng
In comparison with state-of-the-art methods, extensive experiments show that our method achieves consistent better reconstruction performance on the MRI reconstruction in terms of three quantitative metrics (PSNR, SSIM and HFEN) under different undersamling patterns and acceleration factors.
2 code implementations • 14 Aug 2019 • Yongjin Zhou, Weijian Huang, Pei Dong, Yong Xia, Shan-Shan Wang
This function adds a weighted focal coefficient and combines two traditional loss functions.
no code implementations • 6 Aug 2019 • Cheng Li, Hui Sun, Zaiyi Liu, Meiyun Wang, Hairong Zheng, Shan-Shan Wang
From the different modalities, one modality that contributes most to the results is selected as the master modality, which supervises the information selection of the other assistant modalities.
no code implementations • 6 Aug 2019 • Yanxia Chen, Taohui Xiao, Cheng Li, Qiegen Liu, Shan-Shan Wang
Three main contributions have been made: a de-aliasing reconstruction model was proposed to accelerate parallel MR imaging with deep learning exploring both spatial redundancy and multi-coil correlations; a split Bregman iteration algorithm was developed to solve the model efficiently; and unlike most existing parallel imaging methods which rely on the accuracy of the estimated multi-coil sensitivity, the proposed method can perform parallel reconstruction from undersampled data without explicit sensitivity calculation.
2 code implementations • 16 Jul 2019 • Hao Yang, Weijian Huang, Kehan Qi, Cheng Li, Xinfeng Liu, Meiyun Wang, Hairong Zheng, Shan-Shan Wang
To address these challenges, this paper proposes a Cross-Level fusion and Context Inference Network (CLCI-Net) for the chronic stroke lesion segmentation from T1-weighted MR images.
1 code implementation • 16 Jul 2019 • Kehan Qi, Hao Yang, Cheng Li, Zaiyi Liu, Meiyun Wang, Qiegen Liu, Shan-Shan Wang
Recently, approaches based on deep learning and methods for contextual information extraction have served in many image segmentation tasks.
1 code implementation • 11 Jun 2019 • Shan-Shan Wang, Huitao Cheng, Leslie Ying, Taohui Xiao, Ziwen Ke, Xin Liu, Hairong Zheng, Dong Liang
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network.
1 code implementation • 25 Mar 2019 • Lei Zhang, Shan-Shan Wang, Guang-Bin Huang, WangMeng Zuo, Jian Yang, David Zhang
The merits of the proposed MCTL are four-fold: 1) the concept of manifold criterion (MC) is first proposed as a measure validating the distribution matching across domains, and domain adaptation is achieved if the MC is satisfied; 2) the proposed MC can well guide the generation of the intermediate domain sharing similar distribution with the target domain, by minimizing the local domain discrepancy; 3) a global generative discrepancy metric (GGDM) is presented, such that both the global and local discrepancy can be effectively and positively reduced; 4) a simplified version of MCTL called MCTL-S is presented under a perfect domain generation assumption for more generic learning scenario.
no code implementations • 18 Jan 2019 • Ziwen Ke, Shan-Shan Wang, Huitao Cheng, Leslie Ying, Qiegen Liu, Hairong Zheng, Dong Liang
In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhance loss constraint, dubbed as CRDN.
no code implementations • 24 Oct 2018 • Hui Sun, Cheng Li, Boqiang Liu, Hairong Zheng, David Dagan Feng, Shan-Shan Wang
In AUNet, we employ an asymmetrical encoder-decoder structure and propose an effective upsampling block, attention-guided dense-upsampling block (AU block).
no code implementations • 30 Sep 2018 • Shan-Shan Wang, Ziwen Ke, Huitao Cheng, Sen Jia, Ying Leslie, Hairong Zheng, Dong Liang
Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time.
no code implementations • 21 Sep 2018 • Jingxu Xu, Cheng Li, Yongjin Zhou, Lisha Mou, Hairong Zheng, Shan-Shan Wang
Mammographic breast density, a parameter used to describe the proportion of breast tissue fibrosis, is widely adopted as an evaluation characteristic of the likelihood of breast cancer incidence.
1 code implementation • 12 Aug 2018 • Hui Feng, Shan-Shan Wang, Shuzhi Sam Ge
Computer vision based fine-grained recognition has received great attention in recent years.
no code implementations • 1 May 2018 • Hui Feng, Shan-Shan Wang, Shuzhi Sam Ge
Although, the existing approaches are good at action recognition, it is a great challenge to recognize a group of actions in an activity scene.
no code implementations • 24 Nov 2017 • Shan-Shan Wang, Lei Zhang, JingRu Fu
To address the problems of visual domain mismatch, we propose a novel semi-supervised adversarial transfer learning approach, which is called Coupled adversarial transfer Domain Adaptation (CatDA), for distribution alignment between two domains.
no code implementations • 27 Jun 2017 • Shan Guan, Ying Liu, Zhi-Ming Yu, Shan-Shan Wang, Yugui Yao, Shengyuan A. Yang
However, the Dirac points in existing 2D materials, including graphene, are vulnerable against spin-orbit coupling (SOC).
Materials Science
no code implementations • 13 Jun 2017 • Cong Chen, Shan-Shan Wang, Lei Liu, Zhi-Ming Yu, Xian-Lei Sheng, Ziyu Chen, Shengyuan A. Yang
Based on their formation mechanisms, Dirac points in three-dimensional systems can be classified as accidental or essential.
Materials Science
no code implementations • 21 May 2016 • Hongqi Wang, Anfeng Xu, Shan-Shan Wang, Sunny Chughtai
In this paper, we propose a novel transfer learning method for this problem by learning a partially shared classifier for the target domain, and weighting the source domain data points.