Deep learning has been a powerful tool for medical image analysis, but large amount of
high-quality labeled datasets are generally required to train deep learning models with …

Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in
image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI …

Federated learning (FL) has recently gained popularity as a distributed machine learning
approach that protects privacy. However, this concept has not yet been extensively adopted …

The generation of Synthetic Computed Tomography (sCT) images has become a pivotal
methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) …

Independent and identically distributed (iid) data is essential to many data analysis and
modeling techniques. In the medical domain, collecting data from multiple sites or …

This research examines the implementation of the U-Net model within a federated learning
framework, focusing on the semantic segmentation of Diabetic Foot Ulcers (DFUs) images …

Artificial intelligence (AI) technologies have vastly improved. AI is now being used in a
variety of applications to diagnose Breast Cancer (BC). However, most of the recent …

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

The application of Federated Learning (FL) for training neural networks has expanded
significantly in domains requiring sensitive data handling, such as the medical field, yielding …

Melanoma prediction is a skin cancer, and one in seventeen people is affected by this
disease. Identifying and classifying early stages is an important research area in this field …