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 …

The increasing popularity of attention mechanisms in deep learning algorithms for computer
vision and natural language processing made these models attractive to other research …

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 …

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) …

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

Image segmentation achieves significant improvements with deep neural networks at the
premise of a large scale of labeled training data, which is laborious to assure in medical …

Generalizability of deep learning models may be severely affected by the difference in the
distributions of the train (source domain) and the test (target domain) sets, eg, when the sets …

Accurate brain tumor segmentation is critical for diagnosis and treatment planning, whereby
multi-modal magnetic resonance imaging (MRI) is typically used for analysis. However …

The use of deep learning (DL) segmentation in cardiac MRI has the potential to streamline
the radiology workflow, particularly for the measurement of myocardial strain. Recent efforts …

Background: The domain generalization problem has been widely investigated in deep
learning for non-contrast imaging over the last years, but it received limited attention for …