Cited 21 times in
딥러닝 기반 의료 영상 인공지능 모델의 취약성: 적대적 공격
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 정대철 | - |
dc.contributor.author | 최병욱 | - |
dc.date.accessioned | 2019-12-18T01:03:36Z | - |
dc.date.available | 2019-12-18T01:03:36Z | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 1738-2637 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/173346 | - |
dc.description.abstract | Due to rapid developments in the deep learning model, artificial intelligence (AI) models are expected to enhance clinical diagnostic ability and work efficiency by assisting physicians. Therefore, many hospitals and private companies are competing to develop AI-based automatic diagnostic systems using medical images. In the near future, many deep learning-based automatic diagnostic systems would be used clinically. However, the possibility of adversarial attacks exploiting certain vulnerabilities of the deep learning algorithm is a major obstacle to deploying deep learning-based systems in clinical practice. In this paper, we will examine in detail the kinds of principles and methods of adversarial attacks that can be made to deep learning models dealing with medical images, the problems that can arise, and the preventive measures that can be taken against them. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | Korean | - |
dc.publisher | 대한영상의학회 | - |
dc.relation.isPartOf | Journal of the Korean Society of Radiology | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | 딥러닝 기반 의료 영상 인공지능 모델의 취약성: 적대적 공격 | - |
dc.title.alternative | Exploiting the Vulnerability of Deep Learning-Based Artificial Intelligence Models in Medical Imaging: Adversarial Attacks | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | 김휘영 | - |
dc.contributor.googleauthor | 정대철 | - |
dc.contributor.googleauthor | 최병욱 | - |
dc.identifier.doi | 10.3348/jksr.2019.80.2.259 | - |
dc.contributor.localId | A03592 | - |
dc.contributor.localId | A04059 | - |
dc.relation.journalcode | J01843 | - |
dc.identifier.eissn | 2288-2928 | - |
dc.subject.keyword | Deep Learning | - |
dc.subject.keyword | Artificial Intelligence | - |
dc.subject.keyword | Medical Imaging | - |
dc.contributor.alternativeName | Jung, Dae Chul | - |
dc.contributor.affiliatedAuthor | 정대철 | - |
dc.contributor.affiliatedAuthor | 최병욱 | - |
dc.citation.volume | 80 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 259 | - |
dc.citation.endPage | 273 | - |
dc.identifier.bibliographicCitation | Journal of the Korean Society of Radiology, Vol.80(2) : 259-273, 2019 | - |
dc.identifier.rimsid | 64421 | - |
dc.type.rims | ART | - |
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