Open-source distributed learning validation for a larynx cancer survival model following radiotherapy

Hansen, Christian Rønn; Price, Gareth J; Field, Matthew; Sarup, Nis; Zukauskaite, Ruta; Johansen, Jørgen; Eriksen, Jesper Grau; Aly, Farhannah; McPartlin, A.; Holloway, Lois; Thwaites, David; Brink, Carsten

doi:10.1016/j.radonc.2022.06.009

Cited by 8 publications

(1 citation statement)

References 23 publications

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“…FL has been applied to several other fields, and it is growing by the day; however, due to the number within each discipline, we decided to simply mention them in this section. Some of these other fields include ophthalmology [12,[93][94][95], cardiac [96][97][98][99], larynx cancer [100,101], thyroid [102], and tuberculosis [103].…”

Section: Othersmentioning

confidence: 99%

Medical Imaging Applications of Federated Learning

Sandhu,

Gorji,

Tavakolian

et al. 2023

Diagnostics

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Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique’s inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body. This Systematic Literature review was conducted by querying and consolidating results from ArXiv, IEEE Xplorer, and PubMed. Furthermore, we provide a detailed description of FL architecture, models, descriptions of the performance achieved by FL models, and how results compare with traditional Machine Learning (ML) models. Additionally, we discuss the security benefits, highlighting two primary forms of privacy-preserving techniques, including homomorphic encryption and differential privacy. Finally, we provide some background information and context regarding where the contributions lie. The background information is organized into the following categories: architecture/setup type, data-related topics, security, and learning types. While progress has been made within the field of FL and medical imaging, much room for improvement and understanding remains, with an emphasis on security and data issues remaining the primary concerns for researchers. Therefore, improvements are constantly pushing the field forward. Finally, we highlighted the challenges in deploying FL in medical imaging applications and provided recommendations for future directions.

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