한국컴퓨터그래픽스학회논문지 (Journal of the Korea Computer Graphics Society)

  • 제29권2호
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  • Pages.1-11
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  • 2023
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  • 1975-7883(pISSN)
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  • 2383-529X(eISSN)
한국컴퓨터그래픽스학회 (Korea Computer Graphics Society)
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복부 CT 영상에서 심층 합성곱 신경망 기반의 국소 간 병변 분류를 위한 데이터 증강 기법의 효과 분석

Effect of Data Augmentation Techniques for Classification of Focal Liver Lesions Based on Deep Convolutional Neural Networks in Abdominal CT Images

  • 김덕선 (서울여자대학교 소프트웨어융합학과) ;
  • 우아라 (서울여자대학교 소프트웨어융합학과) ;
  • 이한상 (한국과학기술원 정보전자연구소) ;
  • 홍헬렌 (서울여자대학교 소프트웨어융합학과)
  • Deokseon Kim (Department of Software Convergence, Seoul Women's University) ;
  • Ahra Woo (Department of Software Convergence, Seoul Women's University) ;
  • Hansang Lee (School of Electrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Helen Hong (Department of Software Convergence, Seoul Women's University)
  • 투고 : 2022.12.08
  • 심사 : 2023.03.09
  • 발행 : 2023.06.01
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초록

본 논문에서는 복부CT 영상에서 국소 간 병변분류를 위한 심층 합성곱 신경망 학습에 다양한 데이터 증강 기법을 적용하여 의료 영상에서의 효과를 분석한다. 이를 위해 어파인 변환 기반, StyleGAN, Mixup, Augmix 기반 데이터 증강 기법을 각각 VGG16 심층 합성곱 신경망에 적용하여 국소간병변을 낭종, 혈관종, 전이암으로 분류하는 학습을 수행한다. 실험을 위해 각 데이터 증강 기법에 의해 훈련된 모델의 분류 결과에 대하여 정확도, 민감도, 특이도 분석을 통해 정량적 평가를 진행하고, 증강 영상 예시 분석과 tSNE 분석을 통해 정성적 평가를 수행하여 데이터 증강 기법의 효과를 분석한다.

In this paper, we analyze the effects in medical images by applying various data augmentation techniquesto deep convolutional neural network learning for classifying focal liver lesions in abdominal CT images. We apply affine transformation-based, StyleGAN, Mixup, and Augmix-based data augmentation techniquesto the VGG16 convolutional neural network, respectively, to learn to classify local liver lesionsinto cysts, hemangiomas, and metastases. For the experiments, we validate and analyze the effect of the data augmentation through both a quantitative assessment by comparing accuracy, sensitivity, and specificity for classification results of models trained by each data augmentation technique and a qualitative assessment by observing augmented image examples and the tSNE feature distributions.

키워드

과제정보

논문에서 사용한 복부 CT 영상 데이터를 제공해주신 세브란스병원 영상의학과 임준석 교수님께 감사의 말씀을 전합니다. 이 논문은 서울여자대학교 학술연구비의 지원에 의한 것임(2023-0092).

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