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

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 …

Federated learning is an emerging research paradigm enabling collaborative training of
machine learning models among different organizations while keeping data private at each …

Artificial intelligence (AI) has led to a high rate of development in healthcare, and good
progress has been made on many complex medical problems. However, there is a lack of …

U-shaped networks have become prevalent in various medical image tasks such as
segmentation, and restoration. However, most existing U-shaped networks rely on …

Abstract Machine learning has revolutionized every facet of human life, while also becoming
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …

Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

Federated learning (FL) is gaining wide acceptance across the medical AI domains. FL
promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability …

Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment
while preserving patient data privacy and security. Traditional centralized approaches often …

Recent developments in deep learning have contributed to numerous success stories in
healthcare. The performance of a deep learning model generally improves with the size of …