This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …

Deep fully convolutional neural network (FCN) based architectures have shown great
potential in medical image segmentation. However, such architectures usually have millions …

We present a parametric physics-informed neural network for the simulation of personalised
left-ventricular biomechanics. The neural network is constrained to the biophysical problem …

In this paper, a novel algorithm based on a convolutional neural network (CNN) is proposed
for myocardial infarction detection via multilead electrocardiogram (ECG). A beat …

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of
the heart's structure, function, and tissue characteristics with high-resolution spatial …

In many cardiovascular pathologies, the shape and motion of the heart provide important
clues to understanding the mechanisms of the disease and how it progresses over time …

Abstract Deep Implicit Functions (DIFs) represent 3D geometry with continuous signed
distance functions learned through deep neural nets. Recently DIFs-based methods have …

Alkali-activated materials are promising low-carbon alternatives to Portland cement;
however, there remains an absence of a fundamental understanding of the effect of different …

We propose a method to classify cardiac pathology based on a novel approach to extract
image derived features to characterize the shape and motion of the heart. An original semi …

Abstract Changes in mechanical properties of myocardium caused by a infarction can lead
to kinematic abnormalities. This phenomenon has inspired us to develop this work for …