Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …

Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

Federated learning allows distributed medical institutions to collaboratively learn a shared
prediction model with privacy protection. While at clinical deployment, the models trained in …

Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …

Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …

Every year, millions of brain MRI scans are acquired in hospitals, which is a figure
considerably larger than the size of any research dataset. Therefore, the ability to analyze …

Abstract Model generalization capacity at domain shift (eg, various imaging protocols and
scanners) is crucial for deep learning methods in real-world clinical deployment. This paper …

Deep learning models usually suffer from the domain shift issue, where models trained on
one source domain do not generalize well to other unseen domains. In this work, we …