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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 6/2020

01.09.2020 | Original Paper

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

verfasst von: Hanan Farhat, George E. Sakr, Rima Kilany

Erschienen in: Machine Vision and Applications | Ausgabe 6/2020

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Abstract

Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.
Vorheriger Artikel OCLU-NET for occlusal classification of 3D dental models
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1
Cited 1672 times at the time of writing.
 
2
To request credentials for access: [57].
 
3
Hounsfield units used to measure radio-density.
 
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Metadaten
Titel
Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19
verfasst von
Hanan Farhat
George E. Sakr
Rima Kilany
Publikationsdatum
01.09.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 6/2020
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-020-01101-5

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