Federated Learning in Medical Imaging: Part II: Methods, Challenges, and Considerations.

Journal: Journal of the American College of Radiology : JACR
Published Date:

Abstract

Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the privacy of each client's data. Federated learning is instrumental in medical imaging because of the privacy considerations of medical data. Setting up federated networks in hospitals comes with unique challenges, primarily because medical imaging data and federated learning algorithms each have their own set of distinct characteristics. This article introduces federated learning algorithms in medical imaging and discusses technical challenges and considerations of real-world implementation of them.

Authors

  • Erfan Darzidehkalani
    Department of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Machine Learning Lab, Data Science Center in Health, University Medical Center Groningen, University of Groningen, the Netherlands. Electronic address: e.darzidehkalani@umcg.nl.
  • Mohammad Ghasemi-Rad
    Assistant Professor of Radiology, Department of Interventional Radiology, Baylor College of Medicine, Houston, Texas.
  • P M A van Ooijen
    University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, the Netherlands.