13 citations · 54 across the 19 of their papers we have counts for
20 papers
Learned Alternating Minimization Algorithm for Dual-domain Sparse-View CT Reconstruction
Chi Ding, Qingchao Zhang, Ge Wang +2
We propose a novel Learned Alternating Minimization Algorithm (LAMA) for dual-domain sparse-view CT image reconstruction. LAMA is naturally induced by a variational model for CT re…
DULDA: Dual-domain Unsupervised Learned Descent Algorithm for PET image reconstruction
Rui Hu, Yunmei Chen, Kyungsang Kim +3
Deep learning based PET image reconstruction methods have achieved promising results recently. However, most of these methods follow a supervised learning paradigm, which rely heav…
STPDnet: Spatial-temporal convolutional primal dual network for dynamic PET image reconstruction
Rui Hu, Jianan Cui, Chengjin Yu +2
Dynamic positron emission tomography (dPET) image reconstruction is extremely challenging due to the limited counts received in individual frame. In this paper, we propose a spatia…
A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis
Wanyu Bian, Qingchao Zhang, Xiaojing Ye +1
Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information but is limited in practice due to excessive data acquisition time. In this paper, we propos…
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset
Wanyu Bian, Yunmei Chen, Xiaojing Ye +1
Purpose: This work aims at developing a generalizable MRI reconstruction model in the meta-learning framework. The standard benchmarks in meta-learning are challenged by learning o…
An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities
Wanyu Bian, Yunmei Chen, Xiaojing Ye
Goal: This work aims at developing a novel calibration-free fast parallel MRI (pMRI) reconstruction method incorporate with discrete-time optimal control framework. The reconstruct…
Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction
Qingchao Zhang, Mehrdad Alvandipour, Wenjun Xia +3
We propose a provably convergent method, called Efficient Learned Descent Algorithm (ELDA), for low-dose CT (LDCT) reconstruction. ELDA is a highly interpretable neural network arc…
Deep Parallel MRI Reconstruction Network Without Coil Sensitivities
Wanyu Bian, Yunmei Chen, Xiaojing Ye
We propose a novel deep neural network architecture by mapping the robust proximal gradient scheme for fast image reconstruction in parallel MRI (pMRI) with regularization function…
Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction
Yunmei Chen, Hongcheng Liu, Xiaojing Ye +1
We propose a general learning based framework for solving nonsmooth and nonconvex image reconstruction problems. We model the regularization function as the composition of the $l_{…
A Novel Learnable Gradient Descent Type Algorithm for Non-convex Non-smooth Inverse Problems
Qingchao Zhang, Xiaojing Ye, Hongcheng Liu +1
Optimization algorithms for solving nonconvex inverse problem have attracted significant interests recently. However, existing methods require the nonconvex regularization to be sm…
Extra Proximal-Gradient Inspired Non-local Network
Qingchao Zhang, Yunmei Chen
Variational method and deep learning method are two mainstream powerful approaches to solve inverse problems in computer vision. To take advantages of advanced optimization algorit…
Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition
Xiao-Hui Yang, Li Tian, Yun-Mei Chen +3
Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of te…
An Integrated Inverse Space Sparse Representation Framework for Tumor Classification
Xiaohui Yang, Wenming Wu, Yunmei Chen +4
Microarray gene expression data-based tumor classification is an active and challenging issue. In this paper, an integrated tumor classification framework is presented, which aims…
On Kernel Method-Based Connectionist Models and Supervised Deep Learning Without Backpropagation
Shiyu Duan, Shujian Yu, Yunmei Chen +1
We propose a novel family of connectionist models based on kernel machines and consider the problem of learning layer-by-layer a compositional hypothesis class, i.e., a feedforward…
Joint image edge reconstruction and its application in multi-contrast MRI
Yunmei Chen, Ruogu Fang, Xiaojing Ye
We propose a new joint image reconstruction method by recovering edge directly from observed data. More specifically, we reformulate joint image reconstruction with vectorial total…
Fast Bundle-Level Type Methods for unconstrained and ball-constrained convex optimization
Yunmei Chen, Guanghui Lan, Yuyuan Ouyang +1
It has been shown in \cite{Lan13-1} that the accelerated prox-level (APL) method and its variant, the uniform smoothing level (USL) method, have optimal iteration complexity for so…
Accelerated Schemes For A Class of Variational Inequalities
Yunmei Chen, Guanghui Lan, Yuyuan Ouyang
We propose a novel method, namely the accelerated mirror-prox (AMP) method, for computing the weak solutions of a class of deterministic and stochastic monotone variational inequal…
An Accelerated Linearized Alternating Direction Method of Multipliers
Yuyuan Ouyang, Yunmei Chen, Guanghui Lan +1
We present a novel framework, namely AADMM, for acceleration of linearized alternating direction method of multipliers (ADMM). The basic idea of AADMM is to incorporate a multi-ste…
Optimal Primal-Dual Methods for a Class of Saddle Point Problems
Yunmei Chen, Guanghui Lan, Yuyuan Ouyang
We present a novel accelerated primal-dual (APD) method for solving a class of deterministic and stochastic saddle point problems (SPP). The basic idea of this algorithm is to inco…
Projection Onto A Simplex
Yunmei Chen, Xiaojing Ye
This mini-paper presents a fast and simple algorithm to compute the projection onto the canonical simplex . Utilizing the Moreau's identity, we show that the problem i…