Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning.

Journal: The British journal of ophthalmology
Published Date: Jul 23, 2024

Abstract

BACKGROUND: Deep learning (DL) is promising to detect glaucoma. However, patients' privacy and data security are major concerns when pooling all data for model development. We developed a privacy-preserving DL model using the federated learning (FL) paradigm to detect glaucoma from optical coherence tomography (OCT) images.

Authors

  • An Ran Ran
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Xi Wang
    School of Information, Central University of Finance and Economics, Beijing, China.
  • Poemen P Chan
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Hong Kong Eye Hospital, Hong Kong Special Administrative Region, China.
  • Mandy O M Wong
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Hong Kong Eye Hospital, Hong Kong Special Administrative Region, China.
  • Hunter Yuen
    Hong Kong Eye Hospital, Hong Kong SAR, China.
  • Nai Man Lam
    Hong Kong Eye Hospital, Hong Kong SAR, China.
  • Noel C Y Chan
    Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China.
  • Wilson W K Yip
    Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China.
  • Alvin L Young
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Prince of Wales Hospital, Hong Kong Special Administrative Region, China.
  • Hon-Wah Yung
    Tuen Mun Eye Centre, Hong Kong Special Administrative Region, China.
  • Robert T Chang
    Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
  • Suria S Mannil
    Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, USA.
  • Yih-Chung Tham
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore.
  • Ching-Yu Cheng
    Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore.
  • Tien Yin Wong
    Singapore National Eye Center, Duke-National University of Singapore Medical School, Singapore 168751, Singapore; National Institutes of Health Research Biomedical Research Centre Biomedical Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
  • Chi Pui Pang
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong.
  • Pheng-Ann Heng
  • Clement C Tham
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Hong Kong Eye Hospital, Hong Kong Special Administrative Region, China; Prince of Wales Hospital, Hong Kong Special Administrative Region, China.
  • Carol Y Cheung
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China. Electronic address: carolcheung@cuhk.edu.hk.

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