Federated Learning (FL) is becoming increasingly important for advancing machine learning in the medical domain, as it enables collaborative model training across multiple institutions without requiring them to share raw patient data. Unlike traditional centralized machine learning, which gathers data and trains the mo...
Benjamin Hamm, Yannick Kirchhoff, Maximilian R. Rokuss et al.· IEEE journal of biomedical a...· 2 citations
Radiological artificial intelligence has advanced rapidly, yet most systems remain narrowly task-specific, data-intensive, and fragile under domain shift. Foundation models promise more transferable and data-efficient solutions, but existing approaches are limited in scale, evaluated narrowly, and often assume that a s...
C. U. Harsy, Tassilo Wald, Karol Gotkowski et al.· 0 citations
Substantial portions of report text are recoverable from FL gradients even at larger batch sizes and with domain-specific tokenizers, and safeguards such as secure aggregation and differential privacy are likely necessary to meet HIPAA and GDPR requirements for FL in radiology NLP.
Santhosh Parampottupadam, Andres Martinez, D. Bounias et al.· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
