Federated Learning in Ocular Imaging: Current Progress and Future Direction

被引:17
|
作者
Nguyen, Truong X. X. [1 ]
Ran, An Ran [1 ]
Hu, Xiaoyan [1 ]
Yang, Dawei [1 ]
Jiang, Meirui [2 ]
Dou, Qi [2 ]
Cheung, Carol Y. Y. [1 ,3 ]
机构
[1] Chinese Univ Hong Kong, Dept Ophthalmol & Visual Sci, Hong Kong, Peoples R China
[2] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Peoples R China
[3] CUHK Eye Ctr, Kowloon, 4-F Hong Kong Eye Hosp,147K Argyle St, Hong Kong, Peoples R China
关键词
federated learning; deep learning; ocular imaging; ophthalmology; data security; patient privacy; OPTICAL COHERENCE TOMOGRAPHY; ARTIFICIAL-INTELLIGENCE; DIABETIC-RETINOPATHY; OPHTHALMOLOGY; VALIDATION; PREDICTION; CLASSIFICATION; PERFORMANCE; IMAGES;
D O I
10.3390/diagnostics12112835
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the field of ocular imaging over the last few years. Specifically, DL has been utilised to detect and classify various ocular diseases on retinal photographs, optical coherence tomography (OCT) images, and OCT-angiography images. In order to achieve good robustness and generalisability of model performance, DL training strategies traditionally require extensive and diverse training datasets from various sites to be transferred and pooled into a "centralised location". However, such a data transferring process could raise practical concerns related to data security and patient privacy. Federated learning (FL) is a distributed collaborative learning paradigm which enables the coordination of multiple collaborators without the need for sharing confidential data. This distributed training approach has great potential to ensure data privacy among different institutions and reduce the potential risk of data leakage from data pooling or centralisation. This review article aims to introduce the concept of FL, provide current evidence of FL in ocular imaging, and discuss potential challenges as well as future applications.
引用
收藏
页数:14
相关论文
共 50 条
  • [31] Federated learning for medical imaging radiology
    Rehman, Muhammad Habib Ur
    Pinaya, Walter Hugo Lopez
    Nachev, Parashkev
    Teo, James T.
    Ourselin, Sebastin
    Cardoso, M. Jorge
    BRITISH JOURNAL OF RADIOLOGY, 2023, 96 (1150):
  • [32] Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
    Luo, Luyang
    Wang, Xi
    Lin, Yi
    Ma, Xiaoqi
    Tan, Andong
    Chan, Ronald
    Vardhanabhuti, Varut
    Chu, Winnie C. W.
    Cheng, Kwang-Ting
    Chen, Hao
    IEEE REVIEWS IN BIOMEDICAL ENGINEERING, 2025, 18 : 130 - 151
  • [33] Current Techniques and Future Direction Preface
    Cerrato, Rebecca A.
    FOOT AND ANKLE CLINICS, 2015, 20 (01) : XI - XI
  • [34] Current management of neuroblastoma and future direction
    Pastor, Elizabeth R.
    Mousa, Shaker A.
    CRITICAL REVIEWS IN ONCOLOGY HEMATOLOGY, 2019, 138 : 38 - 43
  • [35] Current and future direction in the management of scleroderma
    Brady, Sean M.
    Shapiro, Lee
    Mousa, Shaker A.
    ARCHIVES OF DERMATOLOGICAL RESEARCH, 2016, 308 (07) : 461 - 471
  • [36] Current and future direction in the management of scleroderma
    Sean M. Brady
    Lee Shapiro
    Shaker A. Mousa
    Archives of Dermatological Research, 2016, 308 : 461 - 471
  • [37] Retinoblastoma - Current treatment and future direction
    Parulekar, Manoj V.
    EARLY HUMAN DEVELOPMENT, 2010, 86 (10) : 619 - 625
  • [38] Magnetic Resonance Imaging Biomarkers in Patients with Progressive Ataxia: Current Status and Future Direction
    Currie, Stuart
    Hadjivassiliou, Marios
    Craven, Ian J.
    Wilkinson, Iain D.
    Griffiths, Paul D.
    Hoggard, Nigel
    CEREBELLUM, 2013, 12 (02): : 245 - 266
  • [39] Magnetic Resonance Imaging Biomarkers in Patients with Progressive Ataxia: Current Status and Future Direction
    Stuart Currie
    Marios Hadjivassiliou
    Ian J Craven
    Iain D Wilkinson
    Paul D Griffiths
    Nigel Hoggard
    The Cerebellum, 2013, 12 : 245 - 266
  • [40] FAT: Tilted Federated Learning with Alternating Direction Method of Multipliers
    Cui, Bo
    Yang, Zhen
    PROCEEDINGS OF THE 2024 27 TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN, CSCWD 2024, 2024, : 1801 - 1806