Conventional federated learning (FL) trains one global model for a federation of clients with
decentralized data, reducing the privacy risk of centralized training. However, the distribution …

Federated learning has attracted considerable interest for collaborative machine learning in
healthcare to leverage separate institutional datasets while maintaining patient privacy …

Data plays a vital role in deep learning model training. In large-scale medical image
analysis, data privacy and ownership make data gathering challenging in a centralized …

Artificial intelligence (AI) is a promising technology to transform the healthcare industry. Due
to the highly sensitive nature of patient data, federated learning (FL) is often leveraged to …

Nowadays, deep learning methods with large-scale datasets can produce clinically useful
models for computer-aided diagnosis. However, the privacy and ethical concerns are …

Personalized federated learning (PFL) has emerged as a promising technique for
addressing the challenge of data heterogeneity. While recent studies have made notable …

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

With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …

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 …

Several studies underscore the potential of deep learning in identifying complex patterns,
leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse …