activity
20172022
most citedA multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation

8 citations · 10 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL20221 cited

Coherence-Based Distributed Document Representation Learning for Scientific Documents

Shicheng Tan, Shu Zhao, Yanping Zhang

Distributed document representation is one of the basic problems in natural language processing. Currently distributed document representation methods mainly consider the context i…

eess.IV2021

JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets

Jun Chen, Guang Yang, Habib Khan +7

Automated and accurate segmentations of left atrium (LA) and atrial scars from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are in high demand for quantifyi…

cs.IR20198 cited

A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation

Dong Zhang, Shu Zhao, Zhen Duan +3

Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. How to effectively and accurately recommend reviewers for the su…

cs.LG20191 cited

Discriminative Consistent Domain Generation for Semi-supervised Learning

Jun Chen, Heye Zhang, Yanping Zhang +6

Deep learning based task systems normally rely on a large amount of manually labeled training data, which is expensive to obtain and subject to operator variations. Moreover, it do…

eess.IV2019

Direct Quantification for Coronary Artery Stenosis Using Multiview Learning

Dong Zhang, Guang Yang, Shu Zhao +3

The quantification of the coronary artery stenosis is of significant clinical importance in coronary artery disease diagnosis and intervention treatment. It aims to quantify the mo…

cs.CV2017

Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm

Chenchu Xu, Lei Xu, Zhifan Gao +7

Accurate detection of the myocardial infarction (MI) area is crucial for early diagnosis planning and follow-up management. In this study, we propose an end-to-end deep-learning al…