This thesis explores the application of Federated Learning (FL) in healthcare and medical
imaging, addressing the key challenge of utilizing large, dispersed medical datasets while …

Medical images are private integrations comprising private patient information owned by
various hospitals and relevant research institutes, and the generated image data can be …

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 …

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 …

Deep learning-based medical image analysis is an effective and precise method for
identifying various cancer types. However, due to concerns over patient privacy, sharing …

Federated learning (FL), training deep models from decentralized data without privacy
leakage, has drawn great attention recently. Two common issues in FL, namely data …

Learning from various types of Medical Imaging data have been a area of research since it
involve variation in data distribution bias factor, privacy and legal issues. Hence ML …

Medical diagnostic image analysis (eg, CT scan or X-Ray) using machine learning is an
efficient and accurate way to detect COVID-19 infections. However, the sharing of diagnostic …

Over the last few years, there have been many significant advances in the use of deep
learning in digital pathology. Deep learning has been reported to assist with registration …

Federated learning (FL) enables collaborative training of machine learning models across
distributed medical data sources without compromising privacy. However, applying FL to …