… We summarize existing federated learning studies for medical image analysis in … federated
learning system development are presented. In Section 5, we introduce medical image …

… in medical imaging from the articles. In our findings we briefly presented characteristics of
federated data … the state-of-the-art FL methods for medical image analysis using deep learning. …

… medical data across medical establishments precludes exploiting the full DNN potential for
clinical diagnosis. The federated learning (… federated learning applications in medical image …

… , we explore federated learning (FL) as a collaborative learning … federated learning with
additional privacy preservation techniques can improve the performance of histopathology image …

… , we propose an effective new federated learning framework of HarmoFL. We start with the
formulation of federated heterogeneous medical images analysis, then describe amplitude …

… remain and must be addressed before federated learning is optimally able to build AI models.
Further, because of the novelty of federated learning in medical imaging AI, this topic has …

… with multisource decentralized medical image data. To … federated learning (VAFL)
framework. The key idea is to translate the raw training images of all clients to a predefined image …

… a similar fashion and thus follows the same image supervision level. To relax this … federated
learning framework, named FedMix, for medical image segmentation based on mixed image …

… potential of federated learning in developing multi-domain, multi-task deep learning models
without … In this work, we explore the potential of cross-domain federated learning across two …

… federated learning framework allowing multiple medical institutions screening COVID-19 from
Chest X-ray images using deep learning … two medical image machine learning scenarios: …