Federated learning for medical image analysis with deep neural networks

Sajid Nazir*, Mohammad Kaleem

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

17 Citations (Scopus)
82 Downloads (Pure)

Abstract

Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis. The federated learning (FL) approach enables the use of local model's parameters to train a global model, while ensuring data privacy and security. In this paper, we review the federated learning applications in medical image analysis with DNNs, highlight the security concerns, cover some efforts to improve FL model performance, and describe the challenges and future research directions.

Original languageEnglish
Article number1532
JournalDiagnostics
Volume13
Issue number9
Early online date24 Apr 2023
DOIs
Publication statusPublished - May 2023

Keywords

  • deep neural networks
  • disease diagnosis
  • data privacy
  • model generalization
  • cryptography
  • blockchain

ASJC Scopus subject areas

  • Clinical Biochemistry

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