A Review of Medical Federated Learning: Applications in Oncology and Cancer Research
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Chowdhury2022_Chapter_AReviewOfMedicalFederatedLearn-1.pdf
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675.86 KB
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Author(s)
Chowdhury, Alexander
Kassem, Hasan
Padoy, Nicolas
Umeton, Renato
Karargyris, Alexandros
Date Issued
July 22, 2022
Publisher
Springer International Publishing
Citation
Chowdhury, Alexander, Kassem, Hasan, Padoy, Nicolas, Umeton, Renato and Karargyris, Alexandros. 2022. "A Review of Medical Federated Learning: Applications in Oncology and Cancer Research."
Version
Final published version
Abstract
AbstractMachine learning has revolutionized every facet of human life, while also becoming more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare, with numerous applications and intelligent systems achieving clinical level expertise. However, building robust and generalizable systems relies on training algorithms in a centralized fashion using large, heterogeneous datasets. In medicine, these datasets are time consuming to annotate and difficult to collect centrally due to privacy concerns. Recently, Federated Learning has been proposed as a distributed learning technique to alleviate many of these privacy concerns by providing a decentralized training paradigm for models using large, distributed data. This new approach has become the defacto way of building machine learning models in multiple industries (e.g. edge computing, smartphones). Due to its strong potential, Federated Learning is also becoming a popular training method in healthcare, where patient privacy is of paramount concern. In this paper we performed an extensive literature review to identify state-of-the-art Federated Learning applications for cancer research and clinical oncology analysis. Our objective is to provide readers with an overview of the evolving Federated Learning landscape, with a focus on applications and algorithms in oncology space. Moreover, we hope that this review will help readers to identify potential needs and future directions for research and development.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1007/978-3-031-08999-2_1
