Review Article Open Access

Federated Learning for Analysis of Medical Images: A Survey

Muhammad Imran Sharif1, Mehwish Mehmood2, Md Palash Uddin3,4, Kamran Siddique5, Zahid Akhtar6 and Sadia Waheed7
  • 1 Department of Computer Science, Kansas State University, United States
  • 2 School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, United Kingdom
  • 3 Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
  • 4 School of Information Technology, Deakin University, Geelong, VIC, Australia
  • 5 Department of Computer Science and Engineering, University of Alaska, Anchorage, United States
  • 6 Department of Network and Computer Security, State University of New York Polytechnic Institute, Utica, NY, United States
  • 7 Department of Computer Science, University of Wah, Wah Cantt, Pakistan

Abstract

Machine learning models trained in medical imaging can help in the early detection, diagnosis, and prognosis of the disease. However, it confronts two major obstacles: deep learning models require access to a substantial amount of imaging data, which is a hard constraint, and the patient data is private and sensitive, so it cannot be shared like 1 other imaging data in computer vision. Federated Learning (FL) offers an alternative by deploying many training models in a decentralized way. In recent years, various techniques that leverage FL for disease diagnosis have been introduced. Existing survey articles have analyzed and collated research about the use of FL in general. However, the particular component of medical imaging is ignored. The motivation behind this survey paper is to fill up the research gap by providing a comprehensive survey of FL techniques for medical imaging and various ways in which FL is employed to provide secure, accessible, and collaborative deep learning models for the medical imaging research community.

Journal of Computer Science
Volume 20 No. 12, 2024, 1610-1621

DOI: https://doi.org/10.3844/jcssp.2024.1610.1621

Submitted On: 20 December 2023 Published On: 28 October 2024

How to Cite: Sharif, M. I., Mehmood, M., Uddin, M. P., Siddique, K., Akhtar, Z. & Waheed, S. (2024). Federated Learning for Analysis of Medical Images: A Survey. Journal of Computer Science, 20(12), 1610-1621. https://doi.org/10.3844/jcssp.2024.1610.1621

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Keywords

  • Federated Learning
  • Medical Imaging
  • Classification
  • Segmentation
  • Detection
  • FL Frameworks