Secure, privacy-preserving and federated machine learning in medical imaging
Georgios Kaissis, Marcus R. Makowski, Daniel Rückert low, Rickmer Braren
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Citation edge evidence
- Model-Contrastive Federated Learning→Secure, privacy-preserving and federated machine learning in medical imagingLegacy edge — source detail unavailable
- Federated Learning for Internet of Things: A Comprehensive Survey→Secure, privacy-preserving and federated machine learning in medical imagingLegacy edge — source detail unavailable
- Secure, privacy-preserving and federated machine learning in medical imaging→Mastering the game of Go with deep neural networks and tree searchLegacy edge — source detail unavailable
- Secure, privacy-preserving and federated machine learning in medical imaging→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
- Secure, privacy-preserving and federated machine learning in medical imaging→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
- Secure, privacy-preserving and federated machine learning in medical imaging→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
- Secure, privacy-preserving and federated machine learning in medical imaging→Grandmaster level in StarCraft II using multi-agent reinforcement learningLegacy edge — source detail unavailable
- Secure, privacy-preserving and federated machine learning in medical imaging→International evaluation of an AI system for breast cancer screeningLegacy edge — source detail unavailable
- Secure, privacy-preserving and federated machine learning in medical imaging→Federated Learning: Strategies for Improving Communication EfficiencyLegacy edge — source detail unavailable
- Secure, privacy-preserving and federated machine learning in medical imaging→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
- Secure, privacy-preserving and federated machine learning in medical imaging→Robust De-anonymization of Large Sparse DatasetsLegacy edge — source detail unavailable
- The Algorithmic Foundations of Differential Privacy→Robust De-anonymization of Large Sparse DatasetsLegacy edge — source detail unavailable
- Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures→Robust De-anonymization of Large Sparse DatasetsLegacy edge — source detail unavailable
- Membership Inference Attacks Against Machine Learning Models→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
- Grandmaster level in StarCraft II using multi-agent reinforcement learning→Mastering the game of Go with deep neural networks and tree searchLegacy edge — source detail unavailable
What this paper cites, inside the corpus
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Model-Contrastive Federated Learning | 2021 | 1,485 |
| Federated Learning for Internet of Things: A Comprehensive Survey | 2021 | 1,465 |
Links
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Topics
| Privacy-Preserving Technologies in Data | Computer Science |
| Artificial Intelligence in Healthcare and Education | Medicine |
| Radiomics and Machine Learning in Medical Imaging | Medicine |
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