RISS 학술연구정보서비스

검색
다국어 입력

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

변환된 중국어를 복사하여 사용하시면 됩니다.

예시)
  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
  • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
닫기
    인기검색어 순위 펼치기

    RISS 인기검색어

      KCI등재 SCIE SCOPUS

      Basics of Deep Learning: A Radiologist’s Guide to Understanding Published Radiology Articles on Deep Learning

      한글로보기

      https://www.riss.kr/link?id=A106493209

      • 0

        상세조회
      • 0

        다운로드
      서지정보 열기
      • 내보내기
      • 내책장담기
      • 공유하기
      • 오류접수

      부가정보

      다국어 초록 (Multilingual Abstract)

      Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years. Many articles on deep learn...

      Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years. Many articles on deep learning have been published in radiologic journals. However, radiologists may have difficulty in understanding and interpreting these studies because the study methods of deep learning differ from those of traditional radiology. This review article aims to explain the concepts and terms that are frequently used in deep learning radiology articles, facilitating general radiologists’ understanding.

      더보기

      참고문헌 (Reference)

      1 Jia Y, "https://ui.adsabs.harvard.edu/abs/2014arXiv1408.5093J"

      2 Bastien F, "https://ui.adsabs.harvard.edu/abs/2012arXiv1211.5590B"

      3 Dreyer KJ, "When machines think : radiology’s next frontier" 285 : 713-718, 2017

      4 Cardoso JR, "What is gold standard and what is ground truth?" 19 : 27-30, 2014

      5 Zeiler MD, "Visualizing and understanding convolutional networks"

      6 Simonyan K, "Very deep convolutional networks for large-scale image recognition"

      7 Ronneberger O, "U-net: convolutional networks for biomedical image segmentation"

      8 Abadi M, "TensorFlow: a system for large-scale machine learning"

      9 "TIOBE index for April 2019"

      10 Girshick R, "Rich feature hierarchies for accurate object detection and semantic segmentation"

      1 Jia Y, "https://ui.adsabs.harvard.edu/abs/2014arXiv1408.5093J"

      2 Bastien F, "https://ui.adsabs.harvard.edu/abs/2012arXiv1211.5590B"

      3 Dreyer KJ, "When machines think : radiology’s next frontier" 285 : 713-718, 2017

      4 Cardoso JR, "What is gold standard and what is ground truth?" 19 : 27-30, 2014

      5 Zeiler MD, "Visualizing and understanding convolutional networks"

      6 Simonyan K, "Very deep convolutional networks for large-scale image recognition"

      7 Ronneberger O, "U-net: convolutional networks for biomedical image segmentation"

      8 Abadi M, "TensorFlow: a system for large-scale machine learning"

      9 "TIOBE index for April 2019"

      10 Girshick R, "Rich feature hierarchies for accurate object detection and semantic segmentation"

      11 Zhong Z, "Random erasing data augmentation"

      12 Qian N, "On the momentum term in gradient descent learning algorithms" 12 : 145-151, 1999

      13 He K, "Mask R-CNN"

      14 Krizhevsky A, "ImageNet classification with deep convolutional neural networks" 25 : 1090-1098, 2012

      15 Lecun Y, "Gradient-based learning applied to document recognition" 86 : 2278-2324, 1998

      16 Szegedy C, "Going deeper with convolutions"

      17 Kazuhiro K, "Generative adversarial networks for the creation of realistic artificial brain magnetic resonance images" 4 : 159-163, 2018

      18 Goodfellow IJ, "Generative adversarial networks"

      19 Huang G, "Densely connected convolutional networks"

      20 He K, "Deep residual learning for image recognition"

      21 Chartrand G, "Deep learning : a primer for radiologists" 37 : 2113-2131, 2017

      22 이준구, "Deep Learning in Medical Imaging: General Overview" 대한영상의학회 18 (18): 570-584, 2017

      23 Soffer S, "Convolutional neural networks for radiologic images : a radiologist’s guide" 290 : 590-606, 2019

      24 Lee C, "Automatic disease annotation from radiology reports using artificial intelligence implemented by a recurrent neural network" 212 : 734-740, 2019

      25 Duchi J, "Adaptive subgradient methods for online learning and stochastic optimization" 12 : 2121-2159, 2011

      26 Kingma DP, "Adam: a method for stochastic optimization"

      27 Zeiler MD, "ADADELTA: an adaptive learning rate method"

      28 Litjens G, "A survey on deep learning in medical image analysis" 42 : 60-88, 2017

      29 Nesterov YE, "A method for solving the convex programming problem with convergence rate O(1/k2)" 269 : 543-547, 1983

      더보기

      분석정보

      View

      상세정보조회

      0

      Usage

      원문다운로드

      0

      대출신청

      0

      복사신청

      0

      EDDS신청

      0

      동일 주제 내 활용도 TOP

      더보기

      주제

      연도별 연구동향

      연도별 활용동향

      연관논문

      연구자 네트워크맵

      공동연구자 (7)

      유사연구자 (20) 활용도상위20명

      인용정보 인용지수 설명보기

      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2016-11-15 학회명변경 영문명 : The Korean Radiological Society -> The Korean Society of Radiology KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2007-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2006-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2003-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
      더보기

      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 1.61 0.46 1.15
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.93 0.84 0.494 0.06
      더보기

      이 자료와 함께 이용한 RISS 자료

      나만을 위한 추천자료

      해외이동버튼