COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jiang, Yifan | - |
dc.contributor.author | Chen, Han | - |
dc.contributor.author | Loew, Murray | - |
dc.contributor.author | Ko, Hanseok | - |
dc.date.accessioned | 2021-08-30T03:55:53Z | - |
dc.date.available | 2021-08-30T03:55:53Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2021-02 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/50022 | - |
dc.description.abstract | Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread rapidly since December 2019. Real-time reverse transcription polymerase chain reaction (rRT-PCR) and chest computed tomography (CT) imaging both play an important role in COVID-19 diagnosis. Chest CT imaging offers the benefits of quick reporting, a low cost, and high sensitivity for the detection of pulmonary infection. Recently, deep-learning-based computer vision methods have demonstrated great promise for use in medical imaging applications, including X-rays, magnetic resonance imaging, and CT imaging. However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease. Another issue is the lack of experts available for data labeling. In order to meet the data requirements for COVID-19 CT imaging, we propose a CT image synthesis approach based on a conditional generative adversarial network that can effectively generate high-quality and realistic COVID-19 CT images for use in deep-learning-based medical imaging tasks. Experimental results show that the proposed method outperforms other state-of-the-art image synthesis methods with the generated COVID-19 CT images and indicates promising for various machine learning applications including semantic segmentation and classification. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ko, Hanseok | - |
dc.identifier.doi | 10.1109/JBHI.2020.3042523 | - |
dc.identifier.scopusid | 2-s2.0-85097960653 | - |
dc.identifier.wosid | 000616310200014 | - |
dc.identifier.bibliographicCitation | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.25, no.2, pp.441 - 452 | - |
dc.relation.isPartOf | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | - |
dc.citation.title | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | - |
dc.citation.volume | 25 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 441 | - |
dc.citation.endPage | 452 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | computed topography | - |
dc.subject.keywordAuthor | conditional generative adversarial network | - |
dc.subject.keywordAuthor | image synthesis | - |
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