Search Results for author: Dong Liang

Found 83 papers, 29 papers with code

Diff-DTI: Fast Diffusion Tensor Imaging Using A Feature-Enhanced Joint Diffusion Model

no code implementations24 May 2024 Lang Zhang, Jinling He, Dong Liang, Hairong Zheng, Yanjie Zhu

Diff-DTI introduces a joint diffusion model that directly learns the joint probability distribution of DWIs with DTI parametric maps for conditional generation.

DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models

no code implementations9 May 2024 Mengxiao Geng, Jiahao Zhu, Xiaolin Zhu, Qiqing Liu, Dong Liang, Qiegen Liu

Moreover, virtual binary modal masks are utilized to refine the range of values in k-space data through highly adaptive center windows, which allows the model to focus its attention more efficiently.

Medical Diagnosis MRI Reconstruction

PIE: Physics-inspired Low-light Enhancement

no code implementations6 Apr 2024 Dong Liang, Zhengyan Xu, Ling Li, Mingqiang Wei, Songcan Chen

In this paper, we propose a physics-inspired contrastive learning paradigm for low-light enhancement, called PIE.

Contrastive Learning Face Detection +1

Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta

no code implementations16 Nov 2023 Wei zhang, Dai Li, Chen Liang, Fang Zhou, Zhongke Zhang, Xuewei Wang, Ru Li, Yi Zhou, Yaning Huang, Dong Liang, Kai Wang, Zhangyuan Wang, Zhengxing Chen, Fenggang Wu, Minghai Chen, Huayu Li, Yunnan Wu, Zhan Shu, Mindi Yuan, Sri Reddy

To address these challenges, we present Scaling User Modeling (SUM), a framework widely deployed in Meta's ads ranking system, designed to facilitate efficient and scalable sharing of online user representation across hundreds of ads models.

Representation Learning

A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection

1 code implementation6 Nov 2023 Wenxin Wang, Zhuo-Xu Cui, Guanxun Cheng, Chentao Cao, Xi Xu, Ziwei Liu, Haifeng Wang, Yulong Qi, Dong Liang, Yanjie Zhu

However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution.

Anomaly Detection Generative Adversarial Network +2

InvKA: Gait Recognition via Invertible Koopman Autoencoder

no code implementations26 Sep 2023 Fan Li, Dong Liang, Jing Lian, Qidong Liu, Hegui Zhu, Jizhao Liu

Most current gait recognition methods suffer from poor interpretability and high computational cost.

Gait Recognition

Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI

no code implementations24 Sep 2023 Chen Luo, Huayu Wang, Taofeng Xie, Qiyu Jin, Guoqing Chen, Zhuo-Xu Cui, Dong Liang

However, most of these methods are tailored for supervised learning scenarios that necessitate fully sampled labels, which can pose challenges in practical MRI applications.

Matrix Completion

Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems

no code implementations17 Sep 2023 Huayu Wang, Chen Luo, Taofeng Xie, Qiyu Jin, Guoqing Chen, Zhuo-Xu Cui, Dong Liang

We utilize it as a convex regularizer to formulate a CLEAR-informed variational regularization model that guides the solution of the imaging inverse problem on the real data manifold.

MRI Reconstruction

Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction

1 code implementation2 Sep 2023 Yu Guan, Chuanming Yu, Shiyu Lu, Zhuoxu Cui, Dong Liang, Qiegen Liu

In this study, leveraging a combination of the properties of k-space data and the diffusion process, our novel scheme focuses on mining the multi-frequency prior with different strategies to pre-serve fine texture details in the reconstructed image.

MRI Reconstruction TAR

Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery

1 code implementation31 Aug 2023 Yuyan Zhou, Dong Liang, Songcan Chen, Sheng-Jun Huang, Shuo Yang, Chongyi Li

In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy.

Flare Removal Tone Mapping

Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolation

no code implementations30 Aug 2023 Zhuo-Xu Cui, Congcong Liu, Xiaohong Fan, Chentao Cao, Jing Cheng, Qingyong Zhu, Yuanyuan Liu, Sen Jia, Yihang Zhou, Haifeng Wang, Yanjie Zhu, Jianping Zhang, Qiegen Liu, Dong Liang

In order to enhance interpretability and overcome the acceleration limitations, this paper introduces an interpretable framework that unifies both $k$-space interpolation techniques and image-domain methods, grounded in the physical principles of heat diffusion equations.

Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis

no code implementations5 Aug 2023 Fanshi Li, Zhihui Wang, Yifan Guo, Congcong Liu, Yanjie Zhu, Yihang Zhou, Jun Li, Dong Liang, Haifeng Wang

In this paper, a dynamic dual-graph fusion convolutional network is proposed to improve Alzheimer's disease (AD) diagnosis performance.

Graph Learning

Unsupervised Decomposition Networks for Bias Field Correction in MR Image

1 code implementation30 Jul 2023 Dong Liang, Xingyu Qiu, Kuanquan Wang, Gongning Luo, Wei Wang, Yashu Liu

Many retrospective algorithms were developed to facilitate the bias correction, to which the deep learning-based methods outperformed.

Image Segmentation Segmentation +1

Realistic Restorer: artifact-free flow restorer(AF2R) for MRI motion artifact removal

no code implementations19 Jun 2023 Jiandong Su, Kun Shang, Dong Liang

In this work, we incorporate the artifact generation mechanism to reestablish the relationship between artifacts and anatomical content in the image domain, highlighting the superiority of explicit models over implicit models in medical problems.

RetinexFlow for CT metal artifact reduction

no code implementations18 Jun 2023 Jiandong Su, Ce Wang, Yinsheng Li, Kun Shang, Dong Liang

Metal artifacts is a major challenge in computed tomography (CT) imaging, significantly degrading image quality and making accurate diagnosis difficult.

Computed Tomography (CT) Metal Artifact Reduction

Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis

no code implementations8 Jun 2023 Gongshu Wang, Ning Jiang, Yunxiao Ma, Tiantian Liu, Duanduan Chen, Jinglong Wu, Guoqi Li, Dong Liang, Tianyi Yan

In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis.

Representation Learning

Nonlinear Bipartite Output Regulation with Application to Turing Pattern

no code implementations25 May 2023 Dong Liang, Martin Guay, Shimin Wang

In this paper, a bipartite output regulation problem is solved for a class of nonlinear multi-agent systems subject to static signed communication networks.

Model-driven CT reconstruction algorithm for nano-resolution X-ray phase contrast imaging

no code implementations14 May 2023 Xuebao Cai, Yuhang Tan, Ting Su, Dong Liang, Hairong Zheng, Jinyou Xu, Peiping Zhu, Yongshuai Ge

In conclusion, a novel model-driven nPCT image reconstruction algorithm with high accuracy and robustness is verified for the Lau interferometer based hard X-ray nano-resolution phase contrast imaging.

Computed Tomography (CT) Image Reconstruction

Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI

no code implementations4 May 2023 Zhuo-Xu Cui, Congcong Liu, Chentao Cao, Yuanyuan Liu, Jing Cheng, Qingyong Zhu, Yanjie Zhu, Haifeng Wang, Dong Liang

We theoretically uncovered that the combination of these challenges renders conventional deep learning methods that directly learn the mapping from a low-field MR image to a high-field MR image unsuitable.

Image Reconstruction Meta-Learning

ALL-E: Aesthetics-guided Low-light Image Enhancement

no code implementations28 Apr 2023 Ling Li, Dong Liang, Yuanhang Gao, Sheng-Jun Huang, Songcan Chen

In this paper, we propose a new paradigm, i. e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward.

Low-Light Image Enhancement valid

SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI

no code implementations11 Apr 2023 Zhuo-Xu Cui, Chentao Cao, Yue Wang, Sen Jia, Jing Cheng, Xin Liu, Hairong Zheng, Dong Liang, Yanjie Zhu

To overcome this challenge, we introduce a novel approach called SPIRiT-Diffusion, which is a diffusion model for k-space interpolation inspired by the iterative self-consistent SPIRiT method.

Image Generation MRI Reconstruction

Search By Image: Deeply Exploring Beneficial Features for Beauty Product Retrieval

no code implementations24 Mar 2023 Mingqiang Wei, Qian Sun, Haoran Xie, Dong Liang, Fu Lee Wang

Searching by image is popular yet still challenging due to the extensive interference arose from i) data variations (e. g., background, pose, visual angle, brightness) of real-world captured images and ii) similar images in the query dataset.


Improving Lens Flare Removal with General-Purpose Pipeline and Multiple Light Sources Recovery

1 code implementation ICCV 2023 Yuyan Zhou, Dong Liang, Songcan Chen, Sheng-Jun Huang, Shuo Yang, Chongyi Li

In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy.

Flare Removal Tone Mapping

Universal Generative Modeling in Dual-domain for Dynamic MR Imaging

no code implementations15 Dec 2022 Chuanming Yu, Yu Guan, Ziwen Ke, Dong Liang, Qiegen Liu

Therefore, by taking advantage of the uni-fied framework, we proposed a k-space and image Du-al-Domain collaborative Universal Generative Model (DD-UGM) which combines the score-based prior with low-rank regularization penalty to reconstruct highly under-sampled measurements.

Image Reconstruction

MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection

1 code implementation6 Dec 2022 Dong Liang, Jing-Wei Zhang, Ying-Peng Tang, Sheng-Jun Huang

However, existing active learning methods are mainly with class-balanced settings and image-based querying for generic object detection tasks, which are less applicable to aerial object detection scenarios due to the long-tailed class distribution and dense small objects in aerial scenes.

Active Object Detection Informativeness +3

Deep unfolding as iterative regularization for imaging inverse problems

no code implementations24 Nov 2022 Zhuo-Xu Cui, Qingyong Zhu, Jing Cheng, Dong Liang

Recently, deep unfolding methods that guide the design of deep neural networks (DNNs) through iterative algorithms have received increasing attention in the field of inverse problems.

MRI Reconstruction

ISA-Net: Improved spatial attention network for PET-CT tumor segmentation

no code implementations4 Nov 2022 Zhengyong Huang, Sijuan Zou, Guoshuai Wang, Zixiang Chen, Hao Shen, HaiYan Wang, Na Zhang, Lu Zhang, Fan Yang, Haining Wangg, Dong Liang, Tianye Niu, Xiaohua Zhuc, Zhanli Hua

In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information.

Segmentation STS +1

Active CT Reconstruction with a Learned Sampling Policy

no code implementations3 Nov 2022 Ce Wang, Kun Shang, Haimiao Zhang, Shang Zhao, Dong Liang, S. Kevin Zhou

Experiments on the VerSe dataset demonstrate this ability of our sampling policy, which is difficult to achieve based on uniform sampling.

Computed Tomography (CT) Decision Making

LBF:Learnable Bilateral Filter For Point Cloud Denoising

no code implementations28 Oct 2022 Huajian Si, Zeyong Wei, Zhe Zhu, Honghua Chen, Dong Liang, Weiming Wang, Mingqiang Wei

Bilateral filter (BF) is a fast, lightweight and effective tool for image denoising and well extended to point cloud denoising.

Image Denoising

One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction

2 code implementations15 Aug 2022 Hong Peng, Chen Jiang, Jing Cheng, Minghui Zhang, Shanshan Wang, Dong Liang, Qiegen Liu

At the prior learning stage, we first construct a large Hankel matrix from k-space data, then extract multiple structured k-space patches from the large Hankel matrix to capture the internal distribution among different patches.

K-UNN: k-Space Interpolation With Untrained Neural Network

1 code implementation11 Aug 2022 Zhuo-Xu Cui, Sen Jia, Qingyong Zhu, Congcong Liu, Zhilang Qiu, Yuanyuan Liu, Jing Cheng, Haifeng Wang, Yanjie Zhu, Dong Liang

Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data.

Image Reconstruction

High-Frequency Space Diffusion Models for Accelerated MRI

1 code implementation10 Aug 2022 Chentao Cao, Zhuo-Xu Cui, Yue Wang, Shaonan Liu, Taijin Chen, Hairong Zheng, Dong Liang, Yanjie Zhu

Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation.

Denoising Image Generation +2

Position-prior Clustering-based Self-attention Module for Knee Cartilage Segmentation

1 code implementation21 Jun 2022 Dong Liang, Jun Liu, Kuanquan Wang, Gongning Luo, Wei Wang, Shuo Li

The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic resonance (MR) images and assessed on the cartilage segmentation results.

Clustering Position +1

WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction

1 code implementation8 May 2022 Zongjiang Tu, Die Liu, Xiaoqing Wang, Chen Jiang, Pengwen Zhu, Minghui Zhang, Shanshan Wang, Dong Liang, Qiegen Liu

Deep learning based parallel imaging (PI) has made great progresses in recent years to accelerate magnetic resonance imaging (MRI).

Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging

1 code implementation5 Apr 2022 Hui Tao, Haifeng Wang, Shanshan Wang, Dong Liang, Xiaoling Xu, Qiegen Liu

Parallel imaging is widely used in magnetic resonance imaging as an acceleration technology.

K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction

1 code implementation21 Mar 2022 Zongjiang Tu, Chen Jiang, Yu Guan, Shanshan Wang, Jijun Liu, Qiegen Liu, Dong Liang

Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible.

MRI Reconstruction

Variable Augmented Network for Invertible MR Coil Compression

1 code implementation19 Jan 2022 Xianghao Liao, Shanshan Wang, Lanlan Tu, Yuhao Wang, Dong Liang, Qiegen Liu

Additionally, its performance is not susceptible to different number of virtual coils.

Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI

no code implementations18 Dec 2021 Zhuo-Xu Cui, Jing Cheng, Qingyong Zhu, Yuanyuan Liu, Sen Jia, Kankan Zhao, Ziwen Ke, Wenqi Huang, Haifeng Wang, Yanjie Zhu, Dong Liang

Specifically, focusing on accelerated MRI, we unroll a zeroth-order algorithm, of which the network module represents the regularizer itself, so that the network output can be still covered by the regularization model.

MRI Reconstruction Rolling Shutter Correction

Semantically Contrastive Learning for Low-light Image Enhancement

1 code implementation13 Dec 2021 Dong Liang, Ling Li, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, Huiyu Zhou

Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.

Contrastive Learning Low-Light Image Enhancement +1

Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images

1 code implementation13 Dec 2021 Dong Liang, Qixiang Geng, Zongqi Wei, Dmitry A. Vorontsov, Ekaterina L. Kim, Mingqiang Wei, Huiyu Zhou

On DOTA, our DEA-Net which integrated with the baseline of RoI-Transformer surpasses the advanced method by 0. 40% mean-Average-Precision (mAP) for oriented object detection with a weaker backbone network (ResNet-101 vs ResNet-152) and 3. 08% mean-Average-Precision (mAP) for horizontal object detection with the same backbone.

Ranked #15 on Object Detection In Aerial Images on DOTA (using extra training data)

Object object-detection +4

Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism

no code implementations1 Dec 2021 Minghan Fu, Yanhua Duan, Zhaoping Cheng, Wenjian Qin, Ying Wang, Dong Liang, Zhanli Hu

The derived architecture is referred to as the Teacher-Student Consistency Network (TSC-Net), which consists of the teacher network and the student network with identical architecture.

Contrastive Learning Image Denoising +1

MRI Reconstruction Using Deep Energy-Based Model

1 code implementation7 Sep 2021 Yu Guan, Zongjiang Tu, Shanshan Wang, Qiegen Liu, Yuhao Wang, Dong Liang

In contrast to other generative models for reconstruction, the proposed method utilizes deep energy-based information as the image prior in reconstruction to improve the quality of image.

Image Generation MRI Reconstruction

MPI: Multi-receptive and Parallel Integration for Salient Object Detection

1 code implementation8 Aug 2021 Han Sun, Jun Cen, Ningzhong Liu, Dong Liang, Huiyu Zhou

The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantly improve the model's performance on salient object detection.

Object object-detection +2

A Scalable 256-Elements E-Band Phased-Array Transceiver for Broadband Communication

no code implementations20 Jun 2021 Xu Li, Wenyao Zhai, Morris Repeta, Hua Cai, Tyler Ross, Kimia Ansari, Sam Tiller, Hari Krishna Pothula, Dong Liang, Fan Yang, Yibo Lyu, Songlin Shuai, Guangjian Wang, Wen Tong

For E-band wireless communications, a high gain steerable antenna with sub-arrays is desired to reduce the implementation complexity.

Active Terahertz Imaging Dataset for Concealed Object Detection

1 code implementation8 May 2021 Dong Liang, Fei Xue, Ling Li

Concealed object detection in Terahertz imaging is an urgent need for public security and counter-terrorism.

Object object-detection +1

Learning Calibrated-Guidance for Object Detection in Aerial Images

1 code implementation21 Mar 2021 Zongqi Wei, Dong Liang, Dong Zhang, Liyan Zhang, Qixiang Geng, Mingqiang Wei, Huiyu Zhou

Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance.

Object object-detection +2

Deep Low-rank plus Sparse Network for Dynamic MR Imaging

1 code implementation26 Oct 2020 Wenqi Huang, Ziwen Ke, Zhuo-Xu Cui, Jing Cheng, Zhilang Qiu, Sen Jia, Leslie Ying, Yanjie Zhu, Dong Liang

However, the selection of the parameters of L+S is empirical, and the acceleration rate is limited, which are common failings of iterative compressed sensing MR imaging (CS-MRI) reconstruction methods.

MRI Reconstruction

Is Each Layer Non-trivial in CNN?

no code implementations9 Sep 2020 Wei Wang, Yanjie Zhu, Zhuoxu Cui, Dong Liang

Convolutional neural network (CNN) models have achieved great success in many fields.

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

Exploring the parameter reusability of CNN

no code implementations8 Aug 2020 Wei Wang, Lin Cheng, Yanjie Zhu, Dong Liang

In recent times, using small data to train networks has become a hot topic in the field of deep learning.

Semantic Segmentation Transfer Learning

MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for Mixture of Rain Removal from Single Images

no code implementations21 May 2020 Yiyang Shen, Yidan Feng, Sen Deng, Dong Liang, Jing Qin, Haoran Xie, Mingqiang Wei

We observe three intriguing phenomenons that, 1) rain is a mixture of raindrops, rain streaks and rainy haze; 2) the depth from the camera determines the degrees of object visibility, where objects nearby and faraway are visually blocked by rain streaks and rainy haze, respectively; and 3) raindrops on the glass randomly affect the object visibility of the whole image space.

Generative Adversarial Network Rain Removal

Co-occurrence Background Model with Superpixels for Robust Background Initialization

no code implementations29 Mar 2020 Wenjun Zhou, Yuheng Deng, Bo Peng, Dong Liang, Shun'ichi Kaneko

Background initialization is an important step in many high-level applications of video processing, ranging from video surveillance to video inpainting. However, this process is often affected by practical challenges such as illumination changes, background motion, camera jitter and intermittent movement, etc. In this paper, we develop a co-occurrence background model with superpixel segmentation for robust background initialization.

Segmentation Superpixels

SuperDTI: Ultrafast diffusion tensor imaging and fiber tractography with deep learning

no code implementations3 Feb 2020 Hongyu Li, Zifei Liang, Chaoyi Zhang, Ruiying Liu, Jing Li, Weihong Zhang, Dong Liang, Bowen Shen, Xiaoliang Zhang, Yulin Ge, Jiangyang Zhang, Leslie Ying

We also demonstrate that the trained neural network is robust to noise and motion in the testing data, and the network trained using healthy volunteer data can be directly applied to stroke patient data without compromising the lesion detectability.

An Unsupervised Deep Learning Method for Multi-coil Cine MRI

1 code implementation20 Dec 2019 Ziwen Ke, Jing Cheng, Leslie Ying, Hairong Zheng, Yanjie Zhu, Dong Liang

Although these deep learning methods can improve the reconstruction quality compared with iterative methods without requiring complex parameter selection or lengthy reconstruction time, the following issues still need to be addressed: 1) all these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult to obtain for cardiac MRI; 2) the effect of coil correlation on reconstruction in deep learning methods for dynamic MR imaging has never been studied.

MRI Reconstruction

Learning Priors in High-frequency Domain for Inverse Imaging Reconstruction

1 code implementation23 Oct 2019 Zhuonan He, Jinjie Zhou, Dong Liang, Yuhao Wang, Qiegen Liu

Ill-posed inverse problems in imaging remain an active research topic in several decades, with new approaches constantly emerging.

Denoising Dictionary Learning +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

Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image Reconstruction

1 code implementation3 Sep 2019 Siyuan Wang, Junjie Lv, Yuanyuan Hu, Dong Liang, Minghui Zhang, Qiegen Liu

At the stage of prior learning, transformed feature images obtained by undecimated wavelet transform are stacked as an input of denoising autoencoder network (DAE).

Compressive Sensing Denoising +1

Model Learning: Primal Dual Networks for Fast MR imaging

no code implementations7 Aug 2019 Jing Cheng, Haifeng Wang, Leslie Ying, Dong Liang

Experi-ments on in vivo MR data demonstrate that the proposed method achieves supe-rior MR reconstructions from highly undersampled k-space data over other state-of-the-art image reconstruction methods.

Image Reconstruction

Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks

no code implementations26 Jul 2019 Dong Liang, Jing Cheng, Ziwen Ke, Leslie Ying

Image reconstruction from undersampled k-space data has been playing an important role for fast MRI.

MRI Reconstruction

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

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.

PCGAN: Partition-Controlled Human Image Generation

1 code implementation25 Nov 2018 Dong Liang, Rui Wang, Xiaowei Tian, Cong Zou

Human image generation is a very challenging task since it is affected by many factors.

Image Generation

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

Leveraging Elastic Demand for Forecasting

no code implementations9 Sep 2018 Houtao Deng, Ganesh Krishnan, Ji Chen, Dong Liang

Demand variance can result in a mismatch between planned supply and actual demand.

Tensor RPCA by Bayesian CP Factorization With Complex Noise

no code implementations ICCV 2017 Qiong Luo, Zhi Han, Xi'ai Chen, Yao Wang, Deyu Meng, Dong Liang, Yandong Tang

In this paper, we propose a tensor RPCA model based on CP decomposition and model data noise by Mixture of Gaussians (MoG).


The Apps You Use Bring The Blogs to Follow

no code implementations27 Jun 2016 Shi Yue, Zhong Erheng, Rajan Suju, Dong Liang, Tseng Hao-wei, Li Beitao

Blog recommendation is challenging since most mobile users would suffer from the cold start when there are only a limited number of blogs followed by the user.

G-Bean: an ontology-graph based web tool for biomedical literature retrieval

no code implementations31 Aug 2015 Wang James Z., Zhang Yuanyuan, Dong Liang, Li Lin, Srimani Pradip K, Yu Philip S.

To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. G-Bean addresses PubMed's limitations with three innovations: parallel document index creation, ontology-graph based query expansion, and retrieval and re-ranking of documents based on user's search intention. Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database.

Re-Ranking Retrieval

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