Federated learning: A collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets
Open accessAbstract
Despite the overall success of using artificial intelligence (AI) to assist radiologists in performing computer-aided patient diagnosis, it remains challenging to build good models with small datasets at individual sites. Because many medical images do not come with proper labelling for training, this requires radiologists to perform strenuous labelling work and to prepare the dataset for training. Placing such demands on radiologists is unsustainable, given the ever-increasing number of medical images taken each year. We propose an alternative solution using a relatively new learning framework. This framework, called federated learning, allows individual sites to train a global model in a collaborative effort. Federated learning involves aggregating training results from multiple sites to create a global model without directly sharing datasets. This ensures that patient privacy is maintained across sites. Furthermore, the added supervision obtained from the results of partnering sites improves the global model's overall detection abilities. This alleviates the issue of insufficient supervision when training AI models with small datasets. Lastly, we also address the major challenges of adopting federated learning.
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Together with real-time analytics enabled by GPU/FPGAs, model compression, and edge computing, these developments form a transforming ecosystem in degenerative joint disease management that allows timely, individualized, clinically significant AI interventions.
“Together with real-time analytics enabled by GPU/FPGAs, model compression, and edge computing, these developments form a transforming ecosystem in degenerative joint disease management that allows timely, individualized, clinically significant AI interventions [184] (Table 2).”
From Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease (2025) · cites this paper as [184]
The paper’s conclusion (page not available in our copy) discusses federated learning as a way to improve medical-imaging AI models across institutions. Searches for GPU/FPGAs, model compression, edge computing, and individualized interventions found no passage addressing real-time analytics or degenerative joint disease management. (LLM, conf=0.76)
Experimental — accuracy not yet measuredDespite the general success of AI in diagnosis, it is still challenging to build an effective model with minimal datasets at particular sites.
“Despite the general success of AI in diagnosis, it is still challenging to build an effective model with minimal datasets, at particular sites.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [15]
By collectively building a global model for the prediction of brain age, the FL paradigm in the neuroimaging domain works effectively.
“By collectively building a global model for the prediction of brain age, the FL paradigm in the neuroimaging domain works effectively.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [101]
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The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781 · see citing works
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Privacy-Preserving Federated Brain Tumour Segmentation
Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32692-0_16 · Cited by 578 · see citing works
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Sheller, M., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.04304 · Cited by 20 · see citing works
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Bashir, A. K., Victor, N., Bhattacharya, S., Huynh-The, T., Chengoden, R., Yenduri, G., Maddikunta, P. K. R., Pham, Q.-V., Gadekallu, T. R., & Liyanage, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3304790 · Cited by 183 · see citing works
Applications of Federated Learning; Taxonomy, Challenges, and Research Trends
Shaheen, M., Farooq, M. S., Umer, T., & Kim, B.-S. (2022) · Electronics · doi:10.3390/electronics11040670 · Cited by 157 · see citing works
Naeem, A., Anees, T., Naqvi, R. A., & Loh, W.-K. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020275 · Cited by 103 · see citing works
Federated Learning for Healthcare: A Comprehensive Review
Dhade, P., & Shirke, P. (2024) · RAiSE-2023 · doi:10.3390/engproc2023059230 · Cited by 67 · see citing works
Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey
Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R. M., Sheng, Q. Z., Han, M., Zhao, L., Sannino, G., Ranieri, C. M., Ueyama, J., & Batista, D. M. (2023) · Electronics · doi:10.3390/electronics12122703 · Cited by 51 · see citing works
Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease
Kumar, R., Sporn, K., Borole, A., Khanna, A., Gowda, C., Paladugu, P., Ngo, A., Jagadeesan, R., Zaman, N., & Tavakkoli, A. (2025) · Diagnostics · doi:10.3390/diagnostics15111418 · Cited by 10 · see citing works
“Together with real-time analytics enabled by GPU/FPGAs, model compression, and edge computing, these developments form a transforming ecosystem in degenerative joint disease management that allows timely, individualized, clinically significant AI interventions [184] (Table 2).”
Chen, S.-H., Wu, C.-C., & Chen, K.-F. (2025) · Biomedical Journal · doi:10.1016/j.bj.2025.100853 · Cited by 5 · see citing works
- Authors
- Ng, D., Lan, X., Yao, M. M.-S., Chan, W. P., & Feng, M.
- Affiliations
- National University of Singapore (SG)
- Wan Fang Hospital (TW)
- Corresponding author
- Chan, Wing P.
- Feng, Mengling
- Venue
- Quantitative Imaging in Medicine and Surgery
- Publisher
- AME Publishing Company
- Published
- 2021-02
- Volume
- 11
- Issue
- 2
- Pages
- 852-857
- ISSN
- 2223-4292 (ISSN-L)
- 2223-4306
- PMC
- PMC7779924
- OpenAlex
- W3099081599
- Cited by
- 152
- References
- 6
- Citations by year
- 2026: 17 · 2025: 38 · 2024: 39 · 2023: 33 · 2022: 21 · 2021: 4
- FWCI
- 5.70
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Quantitative Imaging in Medicine and Surgery (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Research Foundation (AISG-GC-2019-002)
- National Research Foundation Singapore (AISG-GC-2019-002)
- National University Health System (WBS R-608-000-199-733)
- National Medical Research Council (HSRG-OC17nov004)
- 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, multi-site collaboration, global model aggregation, radiology, computer-aided diagnosis, limited supervision
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