Cited 1 times in
Artificial Intelligence and Deep Learning in Musculoskeletal Magnetic Resonance Imaging
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김성준 | - |
dc.contributor.author | 송호택 | - |
dc.contributor.author | 이영한 | - |
dc.contributor.author | 이주희 | - |
dc.date.accessioned | 2023-10-19T05:56:04Z | - |
dc.date.available | 2023-10-19T05:56:04Z | - |
dc.date.issued | 2023-06 | - |
dc.identifier.issn | 2384-1095 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/196304 | - |
dc.description.abstract | The application of artificial intelligence (AI) and deep learning (DL) in radiology is rapidly evolving. AI in healthcare has benefits for image recognition, classification, and radiological workflows from a clinical perspective. Additionally, clinical triage AI can be applied to triage systems. This review aims to introduce the concept of DL and discuss its applications in the interpretation of magnetic resonance (MR) images and the DL-based reconstruction of accelerated MR images, with an emphasis on musculoskeletal radiology. The most recent developments and future directions are also discussed briefly. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Korean Society of Magnetic Resonance in Medicine | - |
dc.relation.isPartOf | Investigative Magnetic Resonance Imaging | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Artificial Intelligence and Deep Learning in Musculoskeletal Magnetic Resonance Imaging | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Seung Dae Baek | - |
dc.contributor.googleauthor | Joohee Lee | - |
dc.contributor.googleauthor | Sungjun Kim | - |
dc.contributor.googleauthor | Ho-Taek Song | - |
dc.contributor.googleauthor | Young Han Lee | - |
dc.identifier.doi | 10.13104/imri.2022.1102 | - |
dc.contributor.localId | A00585 | - |
dc.contributor.localId | A02080 | - |
dc.contributor.localId | A02967 | - |
dc.contributor.localId | A04786 | - |
dc.relation.journalcode | J01186 | - |
dc.identifier.eissn | 2384-1109 | - |
dc.subject.keyword | Artificial intelligence | - |
dc.subject.keyword | Deep learning | - |
dc.subject.keyword | Musculoskeletal | - |
dc.subject.keyword | Magnetic resonance imaging | - |
dc.contributor.alternativeName | Kim, Sungjun | - |
dc.contributor.affiliatedAuthor | 김성준 | - |
dc.contributor.affiliatedAuthor | 송호택 | - |
dc.contributor.affiliatedAuthor | 이영한 | - |
dc.contributor.affiliatedAuthor | 이주희 | - |
dc.citation.volume | 27 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 67 | - |
dc.citation.endPage | 74 | - |
dc.identifier.bibliographicCitation | Investigative Magnetic Resonance Imaging, Vol.27(2) : 67-74, 2023-06 | - |
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