The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

Existing communication networks have inherent limitations in translation theory and adapt to
address the complexity of repairing new remote applications at the highest possible level …

Recently, deep networks have shown impressive performance for the segmentation of
cardiac Magnetic Resonance Imaging (MRI) images. However, their achievement is proving …

Background Late gadolinium enhancement (LGE) cardiovascular magnetic resonance
(CMR) imaging is the gold standard for non-invasive myocardial tissue characterisation …

Background: Quantitative cardiovascular magnetic resonance (CMR) T1 mapping has
shown promise for advanced tissue characterisation in routine clinical practise. However, T1 …

This paper presents an automated inspection and classification system for automotive
component remanufacturing industry, based on ensemble learning. The system is based on …

Stress and material deformation field predictions are among the most important tasks in
computational mechanics. These predictions are typically made by solving the governing …

Recent works have introduced methods to estimate segmentation performance without
ground truth, relying solely on neural network softmax outputs. These techniques hold …

Eosinophilic Esophagitis (EoE) represents a challenging condition for medical providers
today. The cause is currently unknown, the impact on a patient's daily life is significant, and it …

Segmentation of 2D echocardiography (2DE) images is an important prerequisite for
quantifying cardiac function. Although deep learning can automate analysis, variability in …