Federated Learning for Analysis of Medical Images: A Survey
- 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.
DOI: https://doi.org/10.3844/jcssp.2024.1610.1621
Copyright: © 2024 Muhammad Imran Sharif, Mehwish Mehmood, Md Palash Uddin, Kamran Siddique, Zahid Akhtar and Sadia Waheed. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Federated Learning
- Medical Imaging
- Classification
- Segmentation
- Detection
- FL Frameworks