Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Uncertainty estimation in healthcare involves quantifying and understanding the inherent
uncertainty or variability associated with medical predictions, diagnoses, and treatment …

Simple Summary Artificial intelligence (AI) technology has been advancing rapidly in recent
years and is being implemented in society. The medical field is no exception, and the clinical …

Generalizing the medical image segmentation algorithms to unseen domains is an important
research topic for computer-aided diagnosis and surgery. Most existing methods require a …

Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …

Abstract Domain adaptation (DA) has drawn high interest for its capacity to adapt a model
trained on labeled source data to perform well on unlabeled or weakly labeled target data …

Automatic semantic segmentation in 2D echocardiography is vital in clinical practice for
assessing various cardiac functions and improving the diagnosis of cardiac diseases …

Deep learning (DL) models have provided state-of-the-art performance in various medical
imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) …

Multi-modality cardiac imaging plays a key role in the management of patients with
cardiovascular diseases. It allows a combination of complementary anatomical …

Generalising deep models to new data from new centres (termed here domains) remains a
challenge. This is largely attributed to shifts in data statistics (domain shifts) between source …