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Multi-Label Image Classification on Long-tailed Optical Coherence Tomography Dataset

긴꼬리 분포의 광간섭 단층촬영 데이터세트에 대한 다중 레이블 이미지 분류

  • Bui, Phuoc-Nguyen (Dept of Superintelligence, Sungkyunkwan University) ;
  • Jung, Kyunghee (Dept of Superintelligence, Sungkyunkwan University) ;
  • Le, Duc-Tai (College of Computing and Informatics, Sungkyunkwan University) ;
  • Choo, Hyunseung (Dept of Electrical and Computer Engineering, Sungkyunkwan University)
  • ;
  • 정경희 (성균관대학교 수퍼인텔리젼스학과) ;
  • ;
  • 추현승 (성균관대학교 전기컴퓨터공학부)
  • Published : 2022.11.21

Abstract

In recent years, retinal disorders have become a serious health concern. Retinal disorders develop slowly and without obvious signs. To avoid vision deterioration, early detection and treatment are critical. Optical coherence tomography (OCT) is a non-invasive and non-contact medical imaging technique used to acquire informative and high-resolution image of retinal area and underlying layers. Disease signs are difficult to detect because OCT images have many areas which are not related to any disease. In this paper, we present a deep learning-based method to perform multi-label classification on a long-tailed OCT dataset. Our method first extracts the region of interest and then performs the classification task. We achieve 98% accuracy, 92% sensitivity, and 99% specificity on our private OCT dataset. Using the heatmap generated from trained convolutional neural network, our method is more robust and explainable than previous approaches because it focuses on areas that contain disease signs.

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