Search Results for author: Shan-Shan Wang

Found 26 papers, 11 papers with code

Fuzzy Discriminant Clustering with Fuzzy Pairwise Constraints

1 code implementation17 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.

Clustering

Homotopic Gradients of Generative Density Priors for MR Image Reconstruction

5 code implementations14 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.

Denoising MRI Reconstruction

A coarse-to-fine framework for unsupervised multi-contrast MR image deformable registration with dual consistency constraint

no code implementations5 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.

Image Registration

Self-adaptive Re-weighted Adversarial Domain Adaptation

no code implementations30 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.

Domain Adaptation

Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising

1 code implementation13 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.

Generative Adversarial Network Image Denoising +1

IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI

2 code implementations24 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.

Compressive Sensing Denoising

LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset

no code implementations24 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.

MRI Reconstruction SSIM

Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation

no code implementations6 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.

Image Segmentation Segmentation +1

Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging

no code implementations6 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.

De-aliasing

CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke

2 code implementations16 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.

Image Segmentation Lesion Segmentation +1

DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution

1 code implementation11 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.

Image Reconstruction

Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation

1 code implementation25 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.

Transfer Learning Unsupervised Domain Adaptation

CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint

no code implementations18 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.

AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms

no code implementations24 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).

Breast Mass Segmentation In Whole Mammograms Segmentation

DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training

no code implementations30 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.

Image Reconstruction

Classifying Mammographic Breast Density by Residual Learning

no code implementations21 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.

Classification General Classification

Object Activity Scene Description, Construction and Recognition

no code implementations1 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.

Action Recognition General Classification +5

Adversarial Transfer Learning for Cross-domain Visual Recognition

no code implementations24 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.

Domain Adaptation Transfer Learning

Two-dimensional Spin-Orbit Dirac Point in Monolayer HfGeTe

no code implementations27 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

Hybrid Dirac Semimetal in CaAgBi Materials Family

no code implementations13 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

Cross Domain Adaptation by Learning Partially Shared Classifiers and Weighting Source Data Points in the Shared Subspaces

no code implementations21 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.

Domain Adaptation Transfer Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.