Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hwang, Youngbae | - |
dc.contributor.author | Park, Junseok | - |
dc.contributor.author | Lim, Yun Jeong | - |
dc.contributor.author | Chun, Hoon Jai | - |
dc.date.accessioned | 2021-09-02T04:58:11Z | - |
dc.date.available | 2021-09-02T04:58:11Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2018-11 | - |
dc.identifier.issn | 2234-2400 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/72415 | - |
dc.description.abstract | Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning-based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning-based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | KOREAN SOC GASTROINTESTINAL ENDOSCOPY | - |
dc.subject | RECOGNITION | - |
dc.subject | IMAGES | - |
dc.subject | SCALE | - |
dc.subject | SHAPE | - |
dc.title | Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now? | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chun, Hoon Jai | - |
dc.identifier.doi | 10.5946/ce.2018.173 | - |
dc.identifier.scopusid | 2-s2.0-85057719067 | - |
dc.identifier.wosid | 000451891200009 | - |
dc.identifier.bibliographicCitation | CLINICAL ENDOSCOPY, v.51, no.6, pp.547 - 551 | - |
dc.relation.isPartOf | CLINICAL ENDOSCOPY | - |
dc.citation.title | CLINICAL ENDOSCOPY | - |
dc.citation.volume | 51 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 547 | - |
dc.citation.endPage | 551 | - |
dc.type.rims | ART | - |
dc.type.docType | Review | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Gastroenterology & Hepatology | - |
dc.relation.journalWebOfScienceCategory | Gastroenterology & Hepatology | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | IMAGES | - |
dc.subject.keywordPlus | SCALE | - |
dc.subject.keywordPlus | SHAPE | - |
dc.subject.keywordAuthor | Capsule endoscopy | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | Lesion detection | - |
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