most citedReview of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

1k citations · 1k across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20211 cited

A channel attention based MLP-Mixer network for motor imagery decoding with EEG

Yanbin He, Zhiyang Lu, Jun Wang +1

Convolutional neural networks (CNNs) and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task. However, these CNN-…

eess.IV2021

Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images

Zhiyang Lu, Zheng Li, Jun Wang +2

The thick-slice magnetic resonance (MR) images are often structurally blurred in coronal and sagittal views, which causes harm to diagnosis and image post-processing. Deep learning…

eess.IV2021

Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography

Ronglin Gong, Jun Wang, Jun Shi

Deep learning can promote the mammography-based computer-aided diagnosis (CAD) for breast cancers, but it generally suffers from the small sample size problem. Self-supervised lear…

eess.IV20201k cited

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

Feng Shi, Jun Wang, Jun Shi +6

(This paper was submitted as an invited paper to IEEE Reviews in Biomedical Engineering on April 6, 2020.) The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over…

cs.CV2020

Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

Fei Shan, Yaozong Gao, Jun Wang +6

CT imaging is crucial for diagnosis, assessment and staging COVID-19 infection. Follow-up scans every 3-5 days are often recommended for disease progression. It has been reported t…