End-to-end privacy preserving deep learning on multi-institutional medical imaging
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The information in a patient's files can be leaked.
“This is also a major issue in the case of medical data wherein the information in a patient's files can be leaked [3], [23].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [23]
Federated learning without additional privacy-enhancing techniques can be reverse-engineered to reconstruct high-fidelity images which encode diagnostic information about patients, such as the absence of a breast indicative of a prior history of breast cancer.
“Prior work demonstrates that federated learning without additional privacy-enhancing techniques can be reverse-engineered to reconstruct high-fidelity images which encode diagnostic information about patients, such as the absence of a breast indicative of a prior history of breast cancer8.”
From Medical imaging deep learning with differential privacy (2021) · cites this paper as [8]
PriMIA is an open-source framework for privacy-preserving decentralized deep learning with medical images.
“The Privacy-preserving Medical Image Analysis (PriMIA) [93] is an open-source framework for privacy-preserving decentralized deep learning with medical images.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [93]
Kaissis et al. [27] proposed a model able to detect different pneumonia samples.
“We also found FL is used in pneumonia detection, Kaissis et al. [27] proposed a model that able to detect different pneumonia samples.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [27]
ResNet18 was used in [23], [26], [27], and [29]; ResNet50 in [18] and [24]; ResNet101 in [24].
“Residual Network (ResNet) is also a commonly used algorithm that can be constructed by different numbers of layers, e.g., ResNet18 ( [23], [26], [27], [29]), ResNet50 ( [18], [24]), ResNet101 ( [24]).”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [27]
Kaissis et al. [27] used two different datasets for training and testing their FL models, both containing X-ray images (same features) but different data.
“Interestingly, Kaissis et al. [27] used two different datasets for training and testing their FL models, the fact is both datasets contain X-ray images (same features) and different data.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [27]
[20] and [27] used two different federated algorithms named Fed and secure aggregation (SecAgg), respectively.
“Likewise, [20] and [27] used two different federated algorithms named Fed, secure aggregation (SecAgg) respectively.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [27]
Kaissis et al. [27] applied both Gaussian and Laplace DP techniques, while Adnan et al. [28] used only Gaussian noise.
“Similarly, Kaissis et al. [27] have applied both techniques and Adnan et al. [28] have used only Gaussian noise in their experiments.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [27]
Article [32] achieved an accuracy of 90.61% and [27] achieved 90%.
“Article [32] and [27] achieved an accuracy of 90.61% and 90% respectively.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [27]
The highest quality score was 9 out of 12, gained by [27], followed by six for [20] and [28] individually.
“Highest score is 9 out of 12 gained by [27], followed by six for [20] and [28] both articles individually.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [27]
Kaissis et al. [27] provided source code online.
“provided source code”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [27]
In Kaissis et al. [27], an additional privacy method is used.
“additional privacy method is used.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [27]
In Kaissis et al. [27], all required sections and experimental details are presented.
“All required sections and experimental de-”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [27]
In Kaissis et al. [27], non-IID data is not considered.
“Non-IID data is not considered.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [27]
FL is a viable method for implementing economic, innovative healthcare systems while ensuring privacy.
“Federated Learning has presented itself as a viable method for implementing economic, innovative healthcare systems while ensuring privacy [11–15].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [13]
FL allows training of AI models through averaging of local updates from numerous healthcare facilities and smart devices, such as IoMT, despite the lack of local data.
“FL allows training of AI models through averaging of local updates from numerous healthcare facilities and smart devices, such as IoMT, despite the lack of local data.”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [13]
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“This is also a major issue in the case of medical data wherein the information in a patient’s files can be leaked [3], [23].”
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Jegorova, M., Kaul, C., Mayor, C., O'Neil, A. Q., Weir, A., Murray-Smith, R., & Tsaftaris, S. A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3229593 · Cited by 77 · see citing works
Zhou, J., Chen, S., Wu, Y., Li, H., Zhang, B., Zhou, L., Hu, Y., Xiang, Z., Li, Z., Chen, N., et al. (2024) · Science Advances · doi:10.1126/sciadv.adh8601 · Cited by 63 · see citing works
A federated learning architecture for secure and private neuroimaging analysis
Stripelis, D., Gupta, U., Saleem, H., Dhinagar, N., Ghai, T., Anastasiou, C., Sánchez, R., Steeg, G. V., Ravi, S., Naveed, M., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101031 · Cited by 31 · see citing works
Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease
Linardos, A., Kushibar, K., Walsh, S., Gkontra, P., & Lekadir, K. (2021) · doi:10.21203/rs.3.rs-688924/v1 · Cited by 9 · see citing works
The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities
Nawaz, A., Edinat, A., Rana, M. R. R., Ali, T., Mustafa, G., Tahir, S., & Lee, S. W. (2026) · Frontiers in public health · doi:10.3389/fpubh.2026.1788454 · Cited by 3 · see citing works
- Authors
- Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M.-M., Saleh, A., Makowski, M., Rueckert, D., & Braren, R.
- Affiliations
- Imperial College London (GB)
- Technical University of Munich (DE)
- Institut national de recherche en sciences et technologies du numérique (FR)
- University of Oxford (GB)
- Universidade Federal de Campina Grande (BR)
- München Klinik Schwabing (DE)
- Corresponding author
- Braren, Rickmer
- ORCID
- Venue
- Nature Machine Intelligence
- Published
- 2021-05-24
- Volume
- 3
- Issue
- 6
- Pages
- 473-484
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3165750456
- Semantic Scholar
- 9bbb61d9bcfa1a2b63efbdb8a521af8c8a2e94ec
- Cited by
- 513
- References
- 65
- Citations by year
- 2026: 100 · 2025: 146 · 2024: 102 · 2023: 80 · 2022: 57 · 2021: 28
- FWCI
- 22.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Funders
- UK Research and Innovation
- Imperial College London
- Deutsche Forschungsgemeinschaft (SPP 2177, FP7/2007–2013, SPP2177/1)
- Technische Universität München
- Deutschen Konsortium für Translationale Krebsforschung (FP7/2007-2013)
- FP7 Ideas: European Research Council (339563)
- 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 › Information Systems › Cloud Data Security Solutions
- Keywords
- privacy-preserving machine learning, federated learning, differential privacy, secure multi-party computation, encrypted inference, diagnostic imaging, model inversion attacks, open source software framework, multi-institutional data sharing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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