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Rethinking Privacy in Medical Imaging AI: From Metadata and Pixel-Level Identification Risks to Federated Learning and Synthetic Data Challenges
2
Zitationen
4
Autoren
2025
Jahr
Abstract
This report reviews methods for preparing imaging data for artificial intelligence applications, focusing on the need for robust privacy protection through de-identification, federated learning, and synthetic data generation, while highlighting the potential risks associated with these approaches.
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