Cited by 103Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and EducationDomain Adaptation and Few-Shot Learning

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

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Darzidehkalani, Erfan, Ghasemi-rad, Mohammad, van Ooijen, P.M.A.·2022·Journal of the American College of Radiology

DOI 10.1016/j.jacr.2022.03.016

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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.

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Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578
Federated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security
(2018) · doi:10.1145/3243734 · Cited by 1,179
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning
Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., & Qi, H. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737416 · Cited by 881
Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security
(2016) · doi:10.1145/2976749 · Cited by 838
A cost-benefit analysis of electronic medical records in primary care
Wang, S. J., Middleton, B., Prosser, L. A., Bardon, C. G., Spurr, C. D., Carchidi, P. J., Kittler, A. F., Goldszer, R. C., Fairchild, D. G., Sussman, A. J., et al. (2003) · The American Journal of Medicine · doi:10.1016/s0002-9343(03)00057-3 · Cited by 740

Show all 31 references

Research works printed but not linked: 4

Wang Z, Hu Q. Blockchain-based federated learning: a comprehensive survey. Preprint. arXiv 2021; 2110.02182.
Li X, Jiang M, Zhang X, Kamp M, Dou Q. Fedbn: Federated learning on non-iid features via local batch normalization. Preprint. arXiv 2021; 2102.07623.
Poirot MG, Vepakomma P, Chang K, Kalpathy-Cramer J, Gupta R, Raskar R. Split learning for collaborative deep learning in healthcare. Preprint. arXiv 2019; 1912.12115.
Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V. Federated learning with non-iid data. Preprint. arXiv 2018; 1806.00582.

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Liu, Y., Huang, J., Chen, J.-C., Chen, W., Pan, Y., & Qiu, J. (2024) · Figshare · doi:10.6084/m9.figshare.25980485
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Anwar, M. S., Khan, A. N., & Raihan, M. (2025) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit68739.2025.11491342
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Bundele, A. D., Deshpande, A. R., Jadhav, S. W., & Handge, Y. A. (2026) · International Conference on Computing, Communication and Green Engineering (CCGE) · doi:10.1109/ccge67142.2026.11581726
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Shepherd, P., Dagiuklas, T., Alkan, B., Bastos, J., & Rodriguez, J. (2026) · International Conference on Communications · doi:10.1109/icc59461.2026.11587338
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Gowtham, M., & P, L. M. (2026) · International Conference on Emerging Research in Smart Electronics and Machine Informatics (ECMI) · doi:10.1109/ecmi68341.2026.11603213
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Authors: Darzidehkalani, E., Ghasemi-rad, M., & van Ooijen, P. M. A.
Affiliations: University Medical Center Groningen (NL)
Baylor College of Medicine (US)
Corresponding author: Darzidehkalani, Erfan
ORCID: Darzidehkalani, Erfan
Ghasemi-rad, Mohammad
Venue: Journal of the American College of Radiology
Publisher: Elsevier BV
Published: 2022-08
Volume: 19
Issue: 8
Pages: 975-982
ISSN: 1546-1440 (ISSN-L)
DOI: 10.1016/j.jacr.2022.03.016
OpenAlex: W4224879845
Cited by: 103
References: 31
Citations by year: 2026: 25 · 2025: 22 · 2024: 35 · 2023: 17 · 2022: 4
FWCI: 6.42
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Journal of the American College of Radiology (publishedVersion, cc-by)
PubMed (publishedVersion)
University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
Funders: Health~Holland
NWO
KWF Kankerbestrijding
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: federated learning, diagnostic imaging, data privacy, decentralized learning, deep neural network, medical data privacy
MeSH terms: Machine Learning, Algorithms, Humans, Neural Networks, Computer, Privacy

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

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