Lei Xing

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Xi'an Jiaotong University ∙ Stanford University

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Xin Wang
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Region-specific Dictionary Learning-based Low-dose Thoracic CT Reconstruction

This paper presents a dictionary learning-based method with region-speci...
10/20/2020 ∙ by Qiong Xu, et al. ∙ 0 ∙ share

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Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images

Computed tomography (CT) has been widely used for medical diagnosis, ass...
09/16/2020 ∙ by Lequan Yu, et al. ∙ 12 ∙ share

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Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis

The automatic diagnosis of various retinal diseases from fundus images i...
07/21/2020 ∙ by Xiaomeng Li, et al. ∙ 14 ∙ share

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Multi-Domain Image Completion for Random Missing Input Data

Multi-domain data are widely leveraged in vision applications taking adv...
07/10/2020 ∙ by Liyue Shen, et al. ∙ 0 ∙ share

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A Mean-Field Theory for Learning the Schönberg Measure of Radial Basis Functions

We develop and analyze a projected particle Langevin optimization method...
06/23/2020 ∙ by Masoud Badiei Khuzani, et al. ∙ 3 ∙ share

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Atlas Based Segmentations via Semi-Supervised Diffeomorphic Registrations

Purpose: Segmentation of organs-at-risk (OARs) is a bottleneck in curren...
11/23/2019 ∙ by Charles Huang, et al. ∙ 7 ∙ share

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Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications

In recent years, significant progress has been made in developing more a...
11/06/2019 ∙ by Hyunseok Seo, et al. ∙ 48 ∙ share

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Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images

Segmentation of livers and liver tumors is one of the most important ste...
10/31/2019 ∙ by Hyunseok Seo, et al. ∙ 38 ∙ share

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A Mean-Field Theory for Kernel Alignment with Random Features in Generative Adversarial Networks

We propose a novel supervised learning method to optimize the kernel in ...
09/25/2019 ∙ by Masoud Badiei Khuzani, et al. ∙ 18 ∙ share

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On Sample Complexity of Projection-Free Primal-Dual Methods for Learning Mixture Policies in Markov Decision Processes

We study the problem of learning policy of an infinite-horizon, discount...
03/15/2019 ∙ by Masoud Badiei Khuzani, et al. ∙ 0 ∙ share

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Multiple Kernel Learning from U-Statistics of Empirical Measures in the Feature Space

We propose a novel data-driven method to learn multiple kernels in kerne...
02/27/2019 ∙ by Masoud Badiei Khuzani, et al. ∙ 0 ∙ share

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Quantized Minimum Error Entropy Criterion

Comparing with traditional learning criteria, such as mean square error ...
10/11/2017 ∙ by Badong Chen, et al. ∙ 0 ∙ share

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Deep Generative Adversarial Networks for Compressed Sensing Automates MRI

Magnetic resonance image (MRI) reconstruction is a severely ill-posed li...
05/31/2017 ∙ by Morteza Mardani, et al. ∙ 0 ∙ share

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Robustness of Maximum Correntropy Estimation Against Large Outliers

The maximum correntropy criterion (MCC) has recently been successfully a...
03/23/2017 ∙ by Badong Chen, et al. ∙ 0 ∙ share

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Robust Learning with Kernel Mean p-Power Error Loss

Correntropy is a second order statistical measure in kernel space, which...
12/21/2016 ∙ by Badong Chen, et al. ∙ 0 ∙ share

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Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering

Nonlinear similarity measures defined in kernel space, such as correntro...
08/01/2016 ∙ by Badong Chen, et al. ∙ 0 ∙ share

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Generalized Correntropy for Robust Adaptive Filtering

As a robust nonlinear similarity measure in kernel space, correntropy ha...
04/12/2015 ∙ by Badong Chen, et al. ∙ 0 ∙ share

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