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Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging
DC Field | Value | Language |
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dc.contributor.author | 양재문 | - |
dc.contributor.author | 이영한 | - |
dc.date.accessioned | 2021-12-28T17:14:51Z | - |
dc.date.available | 2021-12-28T17:14:51Z | - |
dc.date.issued | 2021-03 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/187005 | - |
dc.description.abstract | In recent years, deep learning techniques have been applied in musculoskeletal radiology to increase the diagnostic potential of acquired images. Generative adversarial networks (GANs), which are deep neural networks that can generate or transform images, have the potential to aid in faster imaging by generating images with a high level of realism across multiple contrast and modalities from existing imaging protocols. This review introduces the key architectures of GANs as well as their technical background and challenges. Key research trends are highlighted, including: (a) reconstruction of high-resolution MRI; (b) image synthesis with different modalities and contrasts; (c) image enhancement that efficiently preserves high-frequency information suitable for human interpretation; (d) pixel-level segmentation with annotation sharing between domains; and (e) applications to different musculoskeletal anatomies. In addition, an overview is provided of the key issues wherein clinical applicability is challenging to capture with conventional performance metrics and expert evaluation. When clinically validated, GANs have the potential to improve musculoskeletal imaging. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | RADIOLOGY-ARTIFICIAL INTELLIGENCE | - |
dc.publisher | RADIOLOGY-ARTIFICIAL INTELLIGENCE | - |
dc.relation.isPartOf | RADIOLOGY-ARTIFICIAL INTELLIGENCE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | YiRang Shin | - |
dc.contributor.googleauthor | Jaemoon Yang | - |
dc.contributor.googleauthor | Young Han Lee | - |
dc.identifier.doi | 10.1148/ryai.2021200157 | - |
dc.contributor.localId | A02315 | - |
dc.contributor.localId | A02967 | - |
dc.relation.journalcode | J03846 | - |
dc.identifier.eissn | 2638-6100 | - |
dc.identifier.pmid | 34136816 | - |
dc.subject.keyword | Adults and Pediatrics | - |
dc.subject.keyword | Computer Aided Diagnosis (CAD) | - |
dc.subject.keyword | Computer Applications-General (Informatics) | - |
dc.subject.keyword | Informatics | - |
dc.subject.keyword | Skeletal-Appendicular | - |
dc.subject.keyword | Skeletal-Axial | - |
dc.subject.keyword | Soft Tissues/Skin | - |
dc.contributor.alternativeName | Yang, Jae Moon | - |
dc.contributor.affiliatedAuthor | 양재문 | - |
dc.contributor.affiliatedAuthor | 이영한 | - |
dc.citation.volume | 3 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | e200157 | - |
dc.identifier.bibliographicCitation | RADIOLOGY-ARTIFICIAL INTELLIGENCE, Vol.3(3) : e200157, 2021-03 | - |
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