Federated learning (FL) has emerged with increasing popularity to collaborate distributed
medical institutions for training deep networks. However, despite existing FL algorithms only …

Clinically oriented deep learning algorithms, combined with large-scale medical datasets,
have significantly promoted computer-aided diagnosis. To address increasing ethical and …

Federated learning is an emerging research paradigm for enabling collaboratively training
deep learning models without sharing patient data. However, the data from different …

Multiple medical institutions collaboratively training a model using federated learning (FL)
has become a promising solution for maximizing the potential of data-driven models, yet the …

Conventional federated learning (FL) trains one global model for a federation of clients with
decentralized data, reducing the privacy risk of centralized training. However, the distribution …

Despite recent progress on semi-supervised federated learning (FL) for medical image
diagnosis, the problem of imbalanced class distributions among unlabeled clients is still …

Federated learning is a distributed paradigm that allows multiple parties to collaboratively
train deep models without exchanging the raw data. However, the data distribution among …

The collection and curation of large-scale medical datasets from multiple institutions is
essential for training accurate deep learning models, but privacy concerns often hinder data …

Federated Learning (FL) is a novel approach enabling several clients holding sensitive data
to collaboratively train machine learning models, without centralizing data. The cross-silo FL …

Nowadays, deep learning methods with large-scale datasets can produce clinically useful
models for computer-aided diagnosis. However, the privacy and ethical concerns are …