Limited training data and severe class imbalance impose significant challenges to
developing clinically robust deep learning models. Federated learning (FL) addresses the …

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

The novel coronavirus is spreading rapidly worldwide, and finding an effective and rapid
diagnostic method is apriority. Medical data involves patient privacy, and the centralized …

In Over the last few years, Machine Learning (ML), and particularly Deep Learning (DL), has
made great strides and has been successfully deployed in many real-world applications …

Distributed training can facilitate the processing of large medical image datasets, and
improve the accuracy and efficiency of disease diagnosis while protecting patient privacy …

Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the
field of ocular imaging over the last few years. Specifically, DL has been utilised to detect …

Federated learning is the process of developing machine learning models over datasets
distributed across data centers such as hospitals, clinical research labs, and mobile devices …

Collaboration among multiple data-owning entities (eg, hospitals) can accelerate the
training process and yield better machine learning models due to the availability and …

Federated Learning (FL) has gained significant attention because of its potential for privacy-
preserving distributed learning. However, statistical heterogeneity and label scarcity remain …

One-shot federated learning (FL) has emerged as a promising solution in scenarios where
multiple communication rounds are not practical. Notably, as feature distributions in medical …