This review article discusses the roles of federated learning (FL) and transfer learning (TL) in
cancer detection based on image analysis. These two strategies powered by machine …

Federated learning (FL), a relatively new area of research in medical image analysis,
enables collaborative learning of a federated deep learning model without sharing the data …

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 …

Lung cancer is a disease that affects and causes abnormalities in the lungs. The current
methods to find and treat lung cancer require precise and timely detection to improve patient …

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

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 …

Federated Learning (FL) is a type of distributed deep learning framework in which multiple
devices train a local model using local data, and the gradients of the local model are then …

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 …

Due to the rapid advancements in recent years, medical image analysis is largely dominated
by deep learning (DL). However, building powerful and robust DL models requires training …

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 …