Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis.
Authors
Affiliations (4)
Affiliations (4)
- Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende, Italy.
- Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende, Italy. Electronic address: [email protected].
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende, Italy.
- Radiology Unit, "Annunziata" Hospital, Azienda Ospedaliera di Cosenza, Cosenza, Italy.
Abstract
Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys on Federated Learning in Medical Imaging (FL-MI), published in reputable venues over the past five years. We adopt the PRISMA methodology, categorizing and analyzing the existing body of research in FL-MI. Our analysis identifies common trends, challenges, and emerging strategies for implementing FL in medical imaging, including handling data heterogeneity, privacy concerns, and model performance in non-IID settings. The paper also highlights the most widely used datasets and a comparison of adopted machine learning models. Moreover, we examine FL frameworks in FL-MI applications, such as tumor detection, organ segmentation, and disease classification. We identify several research gaps, including the need for more robust privacy protection. Our findings provide a comprehensive overview of the current state of FL-MI and offer valuable directions for future research and development in this rapidly evolving field.