Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit
privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has …

Federated learning (FL) can be used to improve data privacy and efficiency in magnetic
resonance (MR) image reconstruction by enabling multiple institutions to collaborate without …

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled
data is important in many clinical applications. In recent years, deep learning-based …

Curation of large, diverse MRI datasets via multi-institutional collaborations can help
improve learning of generalizable synthesis models that reliably translate source-onto target …

Deep learning models have shown their advantage in many different tasks, including
neuroimage analysis. However, to effectively train a high-quality deep learning model, the …

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 …

Abstract Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple
hospitals to collaborate distributedly without aggregating local data, thereby protecting …

With recent developments in medical imaging facilities, extensive medical imaging data are
produced every day. This increasing amount of data provides an opportunity for researchers …

Deep learning (DL) algorithms have shown remarkable potential in automating various tasks
in medical imaging and radiologic reporting. However, models trained on low quantities of …

Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …