447 citations · 782 across the 31 of their papers we have counts for
8 papers · 1 filter
Transferable Optimization Network for Cross-Domain Image Reconstruction
Yunmei Chen, Chi Ding, Xiaojing Ye
We develop a novel transfer learning framework to tackle the challenge of limited training data in image reconstruction problems. The proposed framework consists of two training st…
LAMA-Net: A Convergent Network Architecture for Dual-Domain Reconstruction
Chi Ding, Qingchao Zhang, Ge Wang +2
We propose a learnable variational model that learns the features and leverages complementary information from both image and measurement domains for image reconstruction. In parti…
LAMA: Stable Dual-Domain Deep Reconstruction For Sparse-View CT
Chi Ding, Qingchao Zhang, Ge Wang +2
Inverse problems arise in many applications, especially tomographic imaging. We develop a Learned Alternating Minimization Algorithm (LAMA) to solve such problems via two-block opt…
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…
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…
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…
Coarse-to-Fine Classification via Parametric and Nonparametric Models for Computer-Aided Diagnosis
Meizhu Liu, Le Lu, Xiaojing Ye +1
Classification is one of the core problems in Computer-Aided Diagnosis (CAD), targeting for early cancer detection using 3D medical imaging interpretation. High detection sensitivi…