Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation

The novel Coronavirus disease (COVID-19) is a highly contagious virus and has spread all over the world, posing an extremely serious threat to all countries. Automatic lung infection segmentation from computed tomography (CT) plays an important role in the quantitative analysis of COVID-19. However,...

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Bibliographic Details
Main Authors:	Liao, Q. (Author), Xing, J. (Author), Yuan, L. (Author), Zhang, J. (Author), Zhang, Y. (Author), Zhu, H. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	Article benchmarking Benchmarking Biological organs clinical article collaborative learning Collaborative learning Collaborative learning model computed tomography computer assisted tomography Computerized tomography controlled study coronavirus disease 2019 Coronaviruses COVID-19 deep learning Deep learning diagnostic imaging Evaluation metrics feature extraction Feature level few-shot learning ground glass opacity human Humans image segmentation knowledge Knowledge management knowledge transfer Large dataset learning algorithm Learning systems liver tumor lung Lung lung infection Lung infection lung infection segmentation lung lesion lung nodule pleura effusion quantitative analysis SARS-CoV-2 sensitivity and specificity Signal encoding Similarity coefficients State-of-the-art performance Tomography, X-Ray Computed Viruses x-ray computed tomography
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Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation

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