Supervised deep learning needs a large amount of labeled data to achieve high
performance. However, in medical imaging analysis, each site may only have a limited …

Supervised deep learning needs a large amount of labeled data to achieve high
performance. However, in medical imaging analysis, each site may only have a limited …

Medical image segmentation under federated learning (FL) is a promising direction by
allowing multiple clinical sites to collaboratively learn a global model without centralizing …

Personalized federated learning (PFL) addresses the data heterogeneity challenge faced by
general federated learning (GFL). Rather than learning a single global model, with PFL a …

Federated learning is increasingly being explored in the field of medical imaging to train
deep learning models on large scale datasets distributed across different data centers while …

The purpose of federated learning is to enable multiple clients to jointly train a machine
learning model without sharing data. However, the existing methods for training an image …

Cross-silo federated learning (FL) has attracted much attention in medical imaging analysis
with deep learning in recent years as it can resolve the critical issues of insufficient data …

Federated learning (FL) enables collaborative model training while preserving each
participant's privacy, which is particularly beneficial to the medical field. FedAvg is a …

Large training datasets are important for deep learning-based methods. For medical image
segmentation, it could be however difficult to obtain large number of labeled training images …

Existing federated learning works mainly focus on the fully supervised training setting. In
realistic scenarios, however, most clinical sites can only provide data without annotations …