Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …

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 …

Since the rise of deep learning (DL) in the mid-2010s, cardiac magnetic resonance (CMR)
image segmentation has achieved state-of-the-art performance. Despite achieving inter …

Volume-wise labeling in 3D medical images is a time-consuming task that requires
expertise. As a result, there is growing interest in using semi-supervised learning (SSL) …

Deep learning-based segmentation methods provide an effective and automated way for
assessing the structure and function of the heart in cardiac magnetic resonance (CMR) …

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 …

Objective With latest developments in deep learning approaches, automated, accurate, fast,
and generalized segmentation model for left atrium, left ventricle, right ventricle, and …

Training medical image segmentation models usually requires a large amount of labeled
data. By contrast, humans can quickly learn to accurately recognise anatomy of interest from …

For brain tumour segmentation, deep learning models can achieve human expert-level
performance given a large amount of data and pixel-level annotations. However, the …