Accepted: 12 January 2022 Deep Neural networks algorithms are recently used to solve
problems in medical imaging like no time ever. However, one of the main challenges for …

Federated learning is a machine learning method that allows decentralized training of deep
neural networks among multiple clients while preserving the privacy of each client's data …

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 …

Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-
art performance in image classification and segmentation tasks, aiding disease diagnosis …

Medical image analysis is crucial for the efficient diagnosis of many diseases. Typically,
hospitals maintain vast repositories of images, which can be leveraged for various purposes …

Artificial intelligence (AI) holds great promise in healthcare as it can significantly advances
disease diagnosis using diverse medical datasets. However, learning generalizable …

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

Background To develop deep learning models for medical diagnosis, it is important to collect
more medical data from several medical institutions. Due to the regulations for privacy …

Federated Learning (FL) obtained a lot of attention to the academic and industrial
stakeholders from the beginning of its invention. The eye-catching feature of FL is handling …

Artificial intelligence and in particular deep learning have shown great potential in the field
of medical imaging. The models can be used to analyze radiology/pathology images to …