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딥러닝 기반 의료 영상 인공지능 모델의 취약성: 적대적 공격

Other Titles
 Exploiting the Vulnerability of Deep Learning-Based Artificial Intelligence Models in Medical Imaging: Adversarial Attacks 
Authors
 김휘영  ;  정대철  ;  최병욱 
Citation
 Journal of the Korean Society of Radiology, Vol.80(2) : 259-273, 2019 
Journal Title
Journal of the Korean Society of Radiology(대한영상의학회지)
ISSN
 1738-2637 
Issue Date
2019
Keywords
Deep Learning ; Artificial Intelligence ; Medical Imaging
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.
Files in This Item:
T201904521.pdf Download
DOI
10.3348/jksr.2019.80.2.259
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Jung, Dae Chul(정대철) ORCID logo https://orcid.org/0000-0001-5769-5083
Choi, Byoung Wook(최병욱) ORCID logo https://orcid.org/0000-0002-8873-5444
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/173346
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