Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation

Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation

The coronavirus disease 2019 (COVID-19) has become a severe worldwide health emergency and is spreading at a rapid rate. Segmentation of COVID lesions from computed tomography (CT) scans is of great importance for supervising disease progression and further clinical treatment. As labeling COVID-19 C...

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Bibliographic Details
Main Authors: Deng, X. (Author), Deng, Z. (Author), Dong, L. (Author), Dou, Q. (Author), Feng, Z. (Author), Heng, P.-A (Author), Li, C. (Author), Lin, F. (Author), Si, W. (Author), Zhang, K. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
Article
Clinical treatments
Collaborative model
Computed tomography scan
computer assisted tomography
Computerized tomography
coronavirus disease 2019
co-training
COVID-19
COVID-19 CT segmentation
data visualization
Disease progression
Health emergencies
human
Humans
image segmentation
Labeled data
mathematical analysis
SARS-CoV-2
Segmentation methods
self-ensembling model
semi supervised machine learning
semi-supervised image segmentation
Semi-supervised segmentations
sensitivity and specificity
State of the art
supervised machine learning
Supervised Machine Learning
Tomography, X-Ray Computed
x-ray computed tomography
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