Attention-based deep learning system for automated diagnoses of age-related macular degeneration in optical coherence tomography images.

Journal: Medical physics
Published Date: Aug 9, 2021

Abstract

PURPOSE: The progression of age-related macular degeneration (AMD) is critical to treatment decisions in clinical practice. The disease can be classified into four categories namely, drusen, inactive choroidal neovascularization (CNV), active CNV, and normal, according to severity based on optical coherence tomography (OCT) images. Interpreting numerous OCT images is still time-consuming and labor-intensive, especially for the detection of the CNV activity. To address this problem, we developed a deep learning (DL) system based on OCT images, with the assistance of an attention mechanism, to automatically diagnose AMD.

Authors

  • Yan Yan
    Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA.
  • Kai Jin
    Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
  • Zhiyuan Gao
    Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, Zhejiang, China.
  • Xiaoling Huang
    Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, 310009, China.
  • Fanyi Wang
    State Key Laboratory of Modern Optical Instrumentation, Department of Optical Engineering, Zhejiang University, Hangzhou, 310027, China.
  • Yao Wang
    Department of Gastrointestinal Surgery, Zhongshan People's Hospital, Zhongshan, Guangdong, China.
  • Juan Ye
    Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

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