Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging
Nikolas Koutsoubis, Asim Waqas, Yasin Yılmaz, Ravi P. Ramachandran, Matthew B. Schabath, Ghulam Rasool
Abstract
This review article provides an in-depth analysis of the latest advancements in federated learning, privacy preservation, and uncertainty quantification in medical imaging. It also highlights current challenges and explores potential opportunities for improvement in these areas.
Topics & Concepts
Computer scienceFederated learningData sharingArtificial intelligenceData scienceTrustworthinessDeep learningApplications of artificial intelligenceInformation privacyMachine learningInternet privacyMedicinePathologyAlternative medicineRadiomics and Machine Learning in Medical ImagingPrivacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education