Sophisticated diagnostic techniques are required for effective and early identification of lung
cancer, a worldwide health problem. This study explores the creative use of federated …

Deep studying may be used to come across cancer, but it's often constrained by using small,
labelled datasets and a lack of labelling information. To overcome those limitations, switch …

Federated and continual learning are training paradigms addressing data distribution shift in
space and time. More specifically, federated learning tackles non-iid data in space as …

Federated learning is a new machine learning paradigm which allows data parties to build
machine learning models collaboratively while keeping their data secure and private. While …

A major obstacle for cancer research is the prediction of liver cancer progression. This
research looked at models that can predict how Hepatocellular carcinoma (HCC) would …

A significant amount of research has investigated automating medical diagnosis using deep
learning. However, because medical data are collected through diagnostic tests, deep …

Lung cancer is a leading contributor to cancer-related fatalities worldwide, and
histopathological image analysis plays a critical role in cancer detection by identifying …

Federated Learning (FL) aims to learn a single global model that enables the central server
to help the model training in local clients without accessing their local data. The key …

Federated learning (FL) has emerged with increasing popularity in the medical image
analysis field. In collaborative model training, it provides a privacy-preserving scheme by …

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 …