KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD.

Journal: Computer methods and programs in biomedicine
Published Date: Feb 1, 2023

Abstract

BACKGROUND AND OBJECTIVES: Automated diagnosis using deep neural networks can help ophthalmologists detect the blinding eye disease wet Age-related Macular Degeneration (AMD). Wet-AMD has two similar subtypes, Neovascular AMD and Polypoidal Choroidal Vasculopathy (PCV). However, due to the difficulty in data collection and the similarity between images, most studies have only achieved the coarse-grained classification of wet-AMD rather than a fine-grained one of wet-AMD subtypes. Therefore, designing and building a deep learning model to diagnose neovascular AMD and PCV is a great challenge.

Authors

  • Haihong E
    School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: ehaihong@bupt.edu.cn.
  • Jiawen He
    School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: euphy@bupt.edu.cn.
  • Tianyi Hu
    School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: hutianyi@bupt.edu.cn.
  • Lifei Yuan
    Hebei Provincial Eye Hospital, Hebei, 054001, China.
  • Ruru Zhang
    School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: zrr@bupt.edu.cn.
  • Shengjuan Zhang
    Hebei Provincial Eye Hospital, Hebei, 054001, China. Electronic address: 37358641@qq.com.
  • Yanhui Wang
    Department of Traditional Chinese Medicine, Medical College, Xiamen University, Xiamen, China.
  • Meina Song
    School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: mnsong@bupt.edu.cn.
  • Lifei Wang
    Gulf of Maine Research Institute, Portland, ME 04101, USA.

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