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About:
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
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schema:ScholarlyArticle
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wasabi.inria.fr
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Type:
Academic Article
research paper
schema:ScholarlyArticle
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type
Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
has title
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
Creator
Wang, Qian
Wang, Jun
Shi, Feng
Shi, Jun
Shi, F
Wang, J
Shi, J
Wang, )
Shen, D
Shen, Dinggang
Tang, Zhenyu
He, Kelei
Shi, Yinghuan
Wu, Ziyan
Source
ArXiv; Medline
abstract
(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 the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delination of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.
has issue date
2020-04-06
(
xsd:dateTime
)
bibo:doi
10.1109/rbme.2020.2987975
bibo:pmid
32305937
has license
arxiv
sha1sum (hex)
4e95ce827049a350819c5c87caf992f27f9ff792
schema:url
https://doi.org/10.1109/rbme.2020.2987975
resource representing a document's title
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
has PubMed identifier
32305937
schema:publication
IEEE reviews in biomedical engineering
resource representing a document's body
covid:4e95ce827049a350819c5c87caf992f27f9ff792#body_text
is
schema:about
of
named entity 'pandemic'
named entity 'procedure'
named entity 'patients'
named entity 'medical imaging'
named entity 'TRACKING'
named entity 'PATIENTS'
named entity 'IMAGES'
named entity 'GLOBAL'
named entity 'computed tomography'
named entity 'workflow'
named entity 'quantification'
named entity 'contact'
named entity 'accurate'
named entity 'infections'
named entity 'imaging'
named entity 'technicians'
named entity 'COVID'
named entity 'COVID'
named entity 'COVID-19'
named entity 'image acquisition'
named entity 'COVID'
named entity 'COVID-19'
named entity 'COVID'
named entity 'deep learning'
named entity 'pneumonia'
named entity 'U-Net'
named entity 'clinical diagnosis'
named entity 'human body'
named entity 'medRxiv'
named entity 'Convolutional Neural network'
named entity 'image acquisition'
named entity 'U-Net'
named entity 'COVID'
named entity 'lab tests'
named entity 'CT scans'
named entity 'COVID-19'
named entity 'COVID'
named entity 'assay'
named entity 'COVID'
named entity 'COVID'
named entity 'directly related'
named entity 'COVID'
named entity 'image segmentation'
named entity 'follow-up'
named entity 'imaging modality'
named entity 'machine learning-based'
named entity 'COVID'
named entity 'X-ray'
named entity 'chest CT'
named entity 'COVID-19'
named entity 'COVID'
named entity 'COVID'
named entity 'medical images'
named entity 'COVID-19'
named entity 'pneumonia'
named entity 'radiologists'
named entity 'human body'
named entity 'lesion'
named entity 'deep learning'
named entity 'COVID'
named entity 'COVID'
named entity 'COVID'
named entity 'COVID-19'
named entity 'X-ray images'
named entity 'pneumonia'
named entity 'COVID-19'
named entity 'chest CT'
named entity 'depth sensor data'
named entity 'Chest X-Ray'
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