Evaluating a Deep-Learning System for Automatically Calculating the Stroke ASPECT Score
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jung, S.-M. | - |
dc.contributor.author | Whangbo, T.-K. | - |
dc.date.available | 2020-02-27T12:44:08Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4403 | - |
dc.description.abstract | The stroke is one of the leading causes of death around the world. It is a dangerous disease that results in a permanent disability. CT and MRI are representative imaging diagnostic tools for diagnosing the stroke. Particularly, CT has an advantage of examining the disease quickly. The Alberta Stroke Program Early CT Score (ASPECTS) is widely used as a tool to demonstrate the severity of the stroke based on CT images. However, it has a scoring variability issue among medical experts. This study proposed an object and automated ASPECT Score estimation system based on the image processing and deep learning technology for resolving the issue. © 2018 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018 | - |
dc.subject | Computerized tomography | - |
dc.subject | Diagnosis | - |
dc.subject | Image processing | - |
dc.subject | Image segmentation | - |
dc.subject | Magnetic resonance imaging | - |
dc.subject | ASPECT Score | - |
dc.subject | Brain CT | - |
dc.subject | Causes of death | - |
dc.subject | Estimation systems | - |
dc.subject | Imaging diagnostics | - |
dc.subject | Learning technology | - |
dc.subject | Medical experts | - |
dc.subject | Stroke | - |
dc.subject | Deep learning | - |
dc.title | Evaluating a Deep-Learning System for Automatically Calculating the Stroke ASPECT Score | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.1109/ICTC.2018.8539358 | - |
dc.identifier.bibliographicCitation | 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018, pp.564 - 567 | - |
dc.identifier.scopusid | 2-s2.0-85059460853 | - |
dc.citation.endPage | 567 | - |
dc.citation.startPage | 564 | - |
dc.citation.title | 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018 | - |
dc.contributor.affiliatedAuthor | Jung, S.-M. | - |
dc.contributor.affiliatedAuthor | Whangbo, T.-K. | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | ASPECT Score | - |
dc.subject.keywordAuthor | Brain CT | - |
dc.subject.keywordAuthor | Deep-Learning | - |
dc.subject.keywordAuthor | Segmentation | - |
dc.subject.keywordAuthor | Stroke | - |
dc.subject.keywordPlus | Computerized tomography | - |
dc.subject.keywordPlus | Diagnosis | - |
dc.subject.keywordPlus | Image processing | - |
dc.subject.keywordPlus | Image segmentation | - |
dc.subject.keywordPlus | Magnetic resonance imaging | - |
dc.subject.keywordPlus | ASPECT Score | - |
dc.subject.keywordPlus | Brain CT | - |
dc.subject.keywordPlus | Causes of death | - |
dc.subject.keywordPlus | Estimation systems | - |
dc.subject.keywordPlus | Imaging diagnostics | - |
dc.subject.keywordPlus | Learning technology | - |
dc.subject.keywordPlus | Medical experts | - |
dc.subject.keywordPlus | Stroke | - |
dc.subject.keywordPlus | Deep learning | - |
dc.description.journalRegisteredClass | scopus | - |
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