The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Federated Learning in Medical Imaging: Part II: Methods, Challenges, and Considerations
Open accessAbstract
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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“UDA methods force the model to learn domain-agnostic features through adversarial learning [6] or a specific type of batch normalization [7].”
From Memory-aware curriculum federated learning for breast cancer classification (2023) · cites this paper as [7]
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Cites
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
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Federated Optimization in Heterogeneous Networks
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Items 101–103 of 103
Miltykh, I. S., Stashevskaya, N. A., & Burko, P. (2026) · European Radiology · doi:10.1007/s00330-026-12926-5
Rao, V. M., Chen, E., Saenz, A., Banerjee, O., Marklund, H., Bijnens, L., Kim, S. E., Zucker, E. J., Shelmerdine, S. C., Palacio, J. R., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01812-6
Liu, X., & Liu, L. (2026) · Journal of bone oncology · doi:10.1016/j.jbo.2026.100808
Metadata
- 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
- 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)
- 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
- 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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