Journal of the Korea Computer Graphics Society (한국컴퓨터그래픽스학회논문지)

Korea Computer Graphics Society (한국컴퓨터그래픽스학회)
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Assessment and Analysis of Fidelity and Diversity for GAN-based Medical Image Generative Model

GAN 기반 의료영상 생성 모델에 대한 품질 및 다양성 평가 및 분석

  • Jang, Yoojin (Graduate School of Artificial Intelligence, UNIST) ;
  • Yoo, Jaejun (Graduate School of Artificial Intelligence, UNIST) ;
  • Hong, Helen (Dept. of Software Convergence, Seoul Women's University)
  • 장유진 (울산과학기술원 인공지능대학원) ;
  • 유재준 (울산과학기술원 인공지능대학원) ;
  • 홍헬렌 (서울여자대학교 소프트웨어융합학과)
  • Received : 2022.03.08
  • Accepted : 2022.05.23
  • Published : 2022.06.01
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Abstract

Recently, various researches on medical image generation have been suggested, and it becomes crucial to accurately evaluate the quality and diversity of the generated medical images. For this purpose, the expert's visual turing test, feature distribution visualization, and quantitative evaluation through IS and FID are evaluated. However, there are few methods for quantitatively evaluating medical images in terms of fidelity and diversity. In this paper, images are generated by learning a chest CT dataset of non-small cell lung cancer patients through DCGAN and PGGAN generative models, and the performance of the two generative models are evaluated in terms of fidelity and diversity. The performance is quantitatively evaluated through IS and FID, which are one-dimensional score-based evaluation methods, and Precision and Recall, Improved Precision and Recall, which are two-dimensional score-based evaluation methods, and the characteristics and limitations of each evaluation method are also analyzed in medical imaging.

최근 의료영상의 발전에 따라 의료 영상 생성에 대한 다양한 연구가 제안되고 있는데, 이와 관련하여 생성된 의료 영상의 품질과 다양성을 정확하게 평가하는 것이 중요해지고 있다. 생성된 의료 영상을 평가하는 방법으로는 전문가의 시각적 튜링 테스트(visual turing test), 특징 분포 시각화, IS, FID를 통한 정량적 평가를 통해 평가하고 있으나 의료 영상을 품질(fidelity)과 다양성(diversity) 측면에서 정량적으로 평가 하는 방법은 거의 이루어지고 있지 않다. 본 논문에서는 DCGAN과 PGGAN 생성 모델을 통해 비소세포폐암 환자의 흉부 CT 데이터 셋을 학습하여 영상을 생성하고, 이를 품질(fidelity)과 다양성(diversity) 측면에서 두 생성 모델의 성능을 평가한다. 1차원 점수 기반 평가방법인 IS, FID와 2차원 점수 기반 평가방법인 Precision 및 Recall, 개선된 Precision 및 Recall을 통해 성능을 정량적으로 평가하고, 의료영상에서의 각 평가방법들의 특징과 한계점에 대해서도 분석한다.

Keywords

Acknowledgement

본 연구는 서울여자대학교 학술연구비의 지원에 의한 것임(2022-0167).

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