Description

Despite the growing popularity of deep learning neural networks for various medical imaging applications, the vast majority of algorithms to date represent early proof-of-concept designs that will require a degree of evolution before achieving practical clinical utility. In this talk, we will explore several of these key shortcomings and also discuss new emerging research trends that aim to bridge this gap, including topics such as unsupervised and semi-supervised learning, federated learning and clinical implementation challenges.