A systematic review and identification of the challenges of deep learning techniques for undersampled magnetic resonance image reconstruction
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 …
image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI …
Federated vs. Central Machine Learning on Diabetic Foot Ulcer Images: Comparative Simulations
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 …
framework, focusing on the semantic segmentation of Diabetic Foot Ulcers (DFUs) images …
Evaluation of Federated Learning Techniques on Edge Devices Using Synthetic Medical Imaging Datasets
A Alhonainy, P Rao - 2023 IEEE Applied Imagery Pattern …, 2023 - ieeexplore.ieee.org
Artificial intelligence (AI) holds great promise in healthcare as it can significantly advances
disease diagnosis using diverse medical datasets. However, learning generalizable …
disease diagnosis using diverse medical datasets. However, learning generalizable …