Maintaining Privacy in Medical Imaging with Federated Learning, Deep Learning, Differential Privacy, and Encrypted Computation

摘要

The availability of datasets for algorithm training and evaluation is currently hampered due to medical data privacy regulations. The lack of structured electronic medical records and stringent legal criteria has made it difficult for patient data to be collected and shared in a consolidated data lake. For training algorithms, such as convolutional neural networks, this presents difficulties, often requiring vast training examples. To avoid the compromise of patient privacy when encouraging clinical studies on broad datasets aimed at enhancing patient care, it is mandatory to incorporate technological solutions to meet data security and usage criteria at the same time. We present an outline of current and cutting-edge techniques for secure and privacy-preserving artificial intelligence, with an accentuation on medical imaging applications and potential opportunities.