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 …

ABSTRACT The advent of Artificial Intelligence (AI) in healthcare has marked a new era of
medical diagnostics and treatment. Particularly in the field of medical imaging, Deep …

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

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 …

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 …

Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL)
to several domains ranging from edge computing to banking. The technique's inherent …

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 …

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 …

Machine learning and deep learning have demonstrated significant promise for many kinds
of medical imaging applications, including segmentation, classification, and detection. The …

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