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< Page ,Total 47 >
Efficient projected gradient methods for cardinality constrained optimization Scopus CSCD SCIE
期刊论文 | 2019 , 62 (2) , 245-268 | Science China Mathematics
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Abstract :

© 2018 Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature Sparse optimization has attracted increasing attention in numerous areas such as compressed sensing, financial optimization and image processing. In this paper, we first consider a special class of cardinality constrained optimization problems, which involves box constraints and a singly linear constraint. An effcient approach is provided for calculating the projection over the feasibility set after a careful analysis on the projection subproblem. Then we present several types of projected gradient methods for a general class of cardinality constrained optimization problems. Global convergence of the methods are established under suitable assumptions. Finally, we illustrate some applications of the proposed methods for signal recovery and index tracking. Especially for index tracking, we propose a new model subject to an adaptive upper bound on the sparse portfolio weights. The computational results demonstrate that the proposed projected gradient methods are effcient in terms of solution quality.

Keyword :

global convergence index tracking projected gradient method signal recovery sparse approximation

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GB/T 7714 Xu, Fengmin , Dai, Yuhong , Zhao, Zhihu et al. Efficient projected gradient methods for cardinality constrained optimization [J]. | Science China Mathematics , 2019 , 62 (2) : 245-268 .
MLA Xu, Fengmin et al. "Efficient projected gradient methods for cardinality constrained optimization" . | Science China Mathematics 62 . 2 (2019) : 245-268 .
APA Xu, Fengmin , Dai, Yuhong , Zhao, Zhihu , Xu, Zongben . Efficient projected gradient methods for cardinality constrained optimization . | Science China Mathematics , 2019 , 62 (2) , 245-268 .
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An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization SCIE
期刊论文 | 2019 , 38 (2) , 360-370 | IEEE TRANSACTIONS ON MEDICAL IMAGING
WoS CC Cited Count: 2
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Cerebrovascular diseases, i.e., acute stroke, are a common cause of serious long-term disability. Cerebral perfusion computed tomography (CPCT) can provide rapid, high-resolution, quantitative hemodynamic maps to assess and stratify perfusion in patients with acute stroke symptoms. However, CPCT imaging typically involves a substantial radiation dose due to its repeated scanning protocol. Therefore, in this paper, we present a low-dose CPCT image reconstruction method to yield high-quality CPCT images and high-precision hemodynamic maps by utilizing the great similarity information among the repeated scanned CPCT images. Specifically, a newly developed low-rank tensor decomposition with spatial-temporal total variation (LRTD-STTV) regularization is incorporated into the reconstruction model. In the LRTD-STTV regularization, the tensor Tucker decomposition is used to describe global spatial-temporal correlations hidden in the sequential CPCT images, and it is superior to the matricization model (i.e., low-rank model) that fails to fully investigate the prior knowledge of the intrinsic structures of the CPCT images after vectorizing the CPCT images. Moreover, the spatial-temporal TV regularization is used to characterize the local piecewise smooth structure in the spatial domain and the pixels' similarity with the adjacent frames in the temporal domain, because the intensity at each pixel in CPCT images is similar to its neighbors. Therefore, the presented LRTD-STTV model can efficiently deliver faithful underlying information of the CPCT images and preserve the spatial structures. An efficient alternating direction method of multipliers algorithm is also developed to solve the presented LRTD-STTV model. Extensive experimental results on numerical phantom and patient data are clearly demonstrated that the presented model can significantly improve the quality of CPCT images and provide accurate diagnostic features in hemodynamic maps for low-dose cases compared with the existing popular algorithms.

Keyword :

low-rank Computed tomography tensor regularization cerebral perfusion

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GB/T 7714 Li, Sui , Zeng, Dong , Peng, Jiangjun et al. An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization [J]. | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2019 , 38 (2) : 360-370 .
MLA Li, Sui et al. "An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization" . | IEEE TRANSACTIONS ON MEDICAL IMAGING 38 . 2 (2019) : 360-370 .
APA Li, Sui , Zeng, Dong , Peng, Jiangjun , Bian, Zhaoying , Zhang, Hao , Xie, Qi et al. An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization . | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2019 , 38 (2) , 360-370 .
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Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction Scopus SCIE
期刊论文 | 2019 , 38 (2) , 371-382 | IEEE Transactions on Medical Imaging
WoS CC Cited Count: 1 SCOPUS Cited Count: 3
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IEEE Reducing the exposure to X-ray radiation while maintaining a clinically acceptable image quality is desirable in various CT applications. To realize low-dose CT (LdCT) imaging, model-based iterative reconstruction (MBIR) algorithms are widely adopted, but they require proper prior knowledge assumptions in the sinogram and/or image domains and involve tedious manual optimization of multiple parameters. In this work, we propose a deep learning (DL)-based strategy for MBIR to simultaneously address prior knowledge design and MBIR parameter selection in one optimization framework. Specifically, a parameterized plug-and-play alternating direction method of multipliers (3pADMM) is proposed for the general penalized weighted least-squares (PWLS) model, and then, by adopting the basic idea of DL, the parameterized plug-and-play (3p) prior and the related parameters are optimized simultaneously in a single framework using a large number of training data. The main contribution of this work is that the 3p prior and the related parameters in the proposed 3pADMM framework can be supervised and optimized simultaneously to achieve robust LdCT reconstruction performance. Experimental results obtained on clinical patient datasets demonstrate that the proposed method can achieve promising gains over existing algorithms for LdCT image reconstruction in terms of noise-induced artifact suppression and edge detail preservation.

Keyword :

Biomedical imaging Computed tomography deep learning Image reconstruction low-dose CT Machine learning Optimization parameterized plug-and-play ADMM X-ray imaging

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GB/T 7714 He, Ji , Yang, Yan , Wang, Yongbo et al. Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction [J]. | IEEE Transactions on Medical Imaging , 2019 , 38 (2) : 371-382 .
MLA He, Ji et al. "Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction" . | IEEE Transactions on Medical Imaging 38 . 2 (2019) : 371-382 .
APA He, Ji , Yang, Yan , Wang, Yongbo , Zeng, Dong , Bian, Zhaoying , Zhang, Hao et al. Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction . | IEEE Transactions on Medical Imaging , 2019 , 38 (2) , 371-382 .
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A Graph-Based Semisupervised Deep Learning Model for PolSAR Image Classification EI Scopus
期刊论文 | 2018 | IEEE Transactions on Geoscience and Remote Sensing
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Aiming at improving the classification accuracy with limited numbers of labeled pixels in polarimetric synthetic aperture radar (PolSAR) image classification task, this paper presents a graph-based semisupervised deep learning model for PolSAR image classification. It models the PolSAR image as an undirected graph, where the nodes correspond to the labeled and unlabeled pixels, and the weighted edges represent similarities between the pixels. Upon the graph, we design an energy function incorporating a semisupervision term, a convolutional neural network (CNN) term, and a pairwise smoothness term. The employed CNN extracts abstract and data-driven polarimetric features and outputs class label predictions to the graph model. The semisupervision term enforces the category label constraints on the human-labeled pixels. The pairwise smoothness term encourages class label smoothness and the alignment of class label boundaries with the image edges. Starting from an initialized class label map generated based on K-Wishart distribution hypothesis or superpixel segmentation of PauliRGB images, we iteratively and alternately optimize the defined energy function until it converges. We conducted experiments on two real benchmark PolSAR images, and extensive experiments demonstrated that our approach achieved the state-of-the-art results for PolSAR image classification. IEEE

Keyword :

Convolutional Neural Networks (CNN) Graph model Image edge detection Polarimetric synthetic aperture radars Prediction algorithms Semi-supervised method Task analysis

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GB/T 7714 Bi, Haixia , Sun, Jian , Xu, Zongben . A Graph-Based Semisupervised Deep Learning Model for PolSAR Image Classification [J]. | IEEE Transactions on Geoscience and Remote Sensing , 2018 .
MLA Bi, Haixia et al. "A Graph-Based Semisupervised Deep Learning Model for PolSAR Image Classification" . | IEEE Transactions on Geoscience and Remote Sensing (2018) .
APA Bi, Haixia , Sun, Jian , Xu, Zongben . A Graph-Based Semisupervised Deep Learning Model for PolSAR Image Classification . | IEEE Transactions on Geoscience and Remote Sensing , 2018 .
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Preface EI
期刊论文 | 2018 , 945 , V | Communications in Computer and Information Science
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GB/T 7714 Xu, Zongben , Gao, Xinbo , Miao, Qiguang et al. Preface [J]. | Communications in Computer and Information Science , 2018 , 945 : V .
MLA Xu, Zongben et al. "Preface" . | Communications in Computer and Information Science 945 (2018) : V .
APA Xu, Zongben , Gao, Xinbo , Miao, Qiguang , Zhang, Yunquan , Bu, Jiajun . Preface . | Communications in Computer and Information Science , 2018 , 945 , V .
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Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy. PubMed
期刊论文 | 2018 | Bioinformatics
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Abstract :

A large number of recent genome-wide association studies (GWASs) for complex phenotypes confirm the early conjecture for polygenicity, suggesting the presence of large number of variants with only tiny or moderate effects. However, due to the limited sample size of a single GWAS, many associated genetic variants are too weak to achieve the genome-wide significance. These undiscovered variants further limit the prediction capability of GWAS. Restricted access to the individual-level data and the increasing availability of the published GWAS results motivate the development of methods integrating both the individual-level and summary-level data. How to build the connection between the individual-level and summary-level data determines the efficiency of using the existing abundant summary-level resources with limited individual-level data, and this issue inspires more efforts in the existing area.In this study, we propose a novel statistical approach, LEP, which provides a novel way of modeling the connection between the individual-level data and summary-level data. LEP integrates both types of data by LEveraing Pleiotropy to increase the statistical power of risk variants identification and the accuracy of risk prediction. The algorithm for parameter estimation is developed to handle genome-wide-scale data. Through comprehensive simulation studies, we demonstrated the advantages of LEP over the existing methods. We further applied LEP to perform integrative analysis of Crohn's disease from WTCCC and summary statistics from GWAS of some other diseases, such as Type 1 diabetes, Ulcerative colitis and Primary biliary cirrhosis. LEP was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.39% (± 0.58%) to 68.33% (± 0.32%) using about 195,000 variants.The LEP software is available at https://github.com/daviddaigithub/LEP.

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GB/T 7714 Dai Mingwei , Wan Xiang , Peng Hao et al. Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy. [J]. | Bioinformatics , 2018 .
MLA Dai Mingwei et al. "Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy." . | Bioinformatics (2018) .
APA Dai Mingwei , Wan Xiang , Peng Hao , Wang Yao , Liu Yue , Liu Jin et al. Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy. . | Bioinformatics , 2018 .
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Unpaired brain mr-to-ct synthesis using a structure-constrained cyclegan EI
会议论文 | 2018 , 11045 LNCS , 174-182 | 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018 and 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018 Held in Conjunction with MICCAI 2018
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Abstract :

The cycleGAN is becoming an influential method in medical image synthesis. However, due to a lack of direct constraints between input and synthetic images, the cycleGAN cannot guarantee structural consistency between these two images, and such consistency is of extreme importance in medical imaging. To overcome this, we propose a structure-constrained cycleGAN for brain MR-to-CT synthesis using unpaired data that defines an extra structure-consistency loss based on the modality independent neighborhood descriptor to constrain structural consistency. Additionally, we use a position-based selection strategy for selecting training images instead of a completely random selection scheme. Experimental results on synthesizing CT images from brain MR images demonstrate that our method is better than the conventional cycleGAN and approximates the cycleGAN trained with paired data. © Springer Nature Switzerland AG 2018.

Keyword :

Brain MR images CycleGAN Image synthesis Independent neighborhoods MIND Random selection Synthetic images Training image

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GB/T 7714 Yang, Heran , Sun, Jian , Carass, Aaron et al. Unpaired brain mr-to-ct synthesis using a structure-constrained cyclegan [C] . 2018 : 174-182 .
MLA Yang, Heran et al. "Unpaired brain mr-to-ct synthesis using a structure-constrained cyclegan" . (2018) : 174-182 .
APA Yang, Heran , Sun, Jian , Carass, Aaron , Zhao, Can , Lee, Junghoon , Xu, Zongben et al. Unpaired brain mr-to-ct synthesis using a structure-constrained cyclegan . (2018) : 174-182 .
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Learning Through Deterministic Assignment of Hidden Parameters EI
期刊论文 | 2018 | IEEE Transactions on Cybernetics
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Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameters determine the nonlinear mechanism of an estimator, while the bright parameters characterize the linear mechanism. In a traditional learning paradigm, hidden and bright parameters are not distinguished and trained simultaneously in one learning process. Such a one-stage learning (OSL) brings a benefit of theoretical analysis but suffers from the high computational burden. In this paper, we propose a two-stage learning scheme, learning through deterministic assignment of hidden parameters (LtDaHPs), suggesting to deterministically generate the hidden parameters by using minimal Riesz energy points on a sphere and equally spaced points in an interval. We theoretically show that with such a deterministic assignment of hidden parameters, LtDaHP with a neural network realization almost shares the same generalization performance with that of OSL. Then, LtDaHP provides an effective way to overcome the high computational burden of OSL. We present a series of simulations and application examples to support the outperformance of LtDaHP. IEEE

Keyword :

Application examples Bright parameters Generalization performance hidden parameters Learning rates Nonlinear mechanisms Traditional learning Uncertainty

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GB/T 7714 Fang, Jian , Lin, Shaobo , Xu, Zongben . Learning Through Deterministic Assignment of Hidden Parameters [J]. | IEEE Transactions on Cybernetics , 2018 .
MLA Fang, Jian et al. "Learning Through Deterministic Assignment of Hidden Parameters" . | IEEE Transactions on Cybernetics (2018) .
APA Fang, Jian , Lin, Shaobo , Xu, Zongben . Learning Through Deterministic Assignment of Hidden Parameters . | IEEE Transactions on Cybernetics , 2018 .
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Model-driven deep-learning SCIE Scopus CSCD
期刊论文 | 2018 , 5 (1) , 22-24 | NATIONAL SCIENCE REVIEW
WoS CC Cited Count: 2 SCOPUS Cited Count: 2
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GB/T 7714 Xu, Zongben , Sun, Jian . Model-driven deep-learning [J]. | NATIONAL SCIENCE REVIEW , 2018 , 5 (1) : 22-24 .
MLA Xu, Zongben et al. "Model-driven deep-learning" . | NATIONAL SCIENCE REVIEW 5 . 1 (2018) : 22-24 .
APA Xu, Zongben , Sun, Jian . Model-driven deep-learning . | NATIONAL SCIENCE REVIEW , 2018 , 5 (1) , 22-24 .
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A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization EI SCIE Scopus
期刊论文 | 2018 , 29 (5) , 1716-1731 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
WoS CC Cited Count: 1 SCOPUS Cited Count: 2
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Iterative thresholding is a dominating strategy for sparse optimization problems. The main goal of iterative thresh-olding methods is to find a so-called k-sparse solution. However, the setting of regularization parameters or the estimation of the true sparsity are nontrivial in iterative thresholding methods. To overcome this shortcoming, we propose a preference-based multiobjective evolutionary approach to solve sparse optimization problems in compressive sensing. Our basic strategy is to search the knee part of weakly Pareto front with preference on the true k-sparse solution. In the noiseless case, it is easy to locate the exact position of the k-sparse solution from the distribution of the solutions found by our proposed method. Therefore, our method has the ability to detect the true sparsity. Moreover, any iterative thresholding methods can be used as a local optimizer in our proposed method, and no prior estimation of sparsity is required. The proposed method can also be extended to solve sparse optimization problems with noise. Extensive experiments have been conducted to study its performance on artificial signals and magnetic resonance imaging signals. Our experimental results have shown that our proposed method is very effective for detecting sparsity and can improve the reconstruction ability of existing iterative thresholding methods.

Keyword :

multiobjective evolutionary approach regularization Sparse optimization iterative thresholding

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GB/T 7714 Li, Hui , Zhang, Qingfu , Deng, Jingda et al. A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2018 , 29 (5) : 1716-1731 .
MLA Li, Hui et al. "A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29 . 5 (2018) : 1716-1731 .
APA Li, Hui , Zhang, Qingfu , Deng, Jingda , Xu, Zong-Ben . A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2018 , 29 (5) , 1716-1731 .
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