BFENet: A two-stream interaction CNN method for multi-label ophthalmic diseases classification with bilateral fundus images.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 1, 2022

Abstract

BACKGROUND AND OBJECTIVE: Early fundus screening and timely treatment of ophthalmology diseases can effectively prevent blindness. Previous studies just focus on fundus images of single eye without utilizing the useful relevant information of the left and right eyes. While clinical ophthalmologists usually use binocular fundus images to help ocular disease diagnosis. Besides, previous works usually target only one ocular diseases at a time. Considering the importance of patient-level bilateral eye diagnosis and multi-label ophthalmic diseases classification, we propose a bilateral feature enhancement network (BFENet) to address the above two problems.

Authors

  • Xingyuan Ou
    College of Artificial Intelligence, Nankai University, Tianjin, China.
  • Li Gao
    College of Veterinary Medicine, Northeast Agricultural University, Harbin 150000, China.
  • Xiongwen Quan
    College of Artificial Intelligence, Nankai University, Tongyan Road, Tianjin, 300350, China.
  • Han Zhang
    Johns Hopkins University, Baltimore, MD, USA.
  • Jinglong Yang
    College of Artificial Intelligence, Nankai University, Tianjin, China.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.

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