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 …

Federated Learning (FL) is a decentralized machine learning framework that builds a shared
model by distributing training data across mobile devices and aggregating updates from …

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 …

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 …

Abstract Machine learning has revolutionized research by extracting complicated patterns
from complex data, particularly in healthcare and medical imaging, where accurate …

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 …

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 (FL) could solve the challenges of training AI models on large datasets
for medical imaging due to data privacy and ownership concerns by allowing collaborative …

Federated learning (FL) enables collaboratively training a joint model for multiple medical
centers, while keeping the data decentralized due to privacy concerns. However, federated …

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 …