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Antonio de Marvao

Researcher at Imperial College London

Publications -  94
Citations -  4448

Antonio de Marvao is an academic researcher from Imperial College London. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 25, co-authored 80 publications receiving 3062 citations. Previous affiliations of Antonio de Marvao include Medical Research Council & University of Paris.

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Journal ArticleDOI

Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

TL;DR: In this article, a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularization model, which is trained end-to-end, encourages models to follow the global anatomical properties of the underlying anatomy via learnt non-linear representations of the shape.
Journal ArticleDOI

Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

TL;DR: This work proposes a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end and demonstrates how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.
Journal ArticleDOI

Integrated allelic, transcriptional, and phenomic dissection of the cardiac effects of titin truncations in health and disease

Angharad M. Roberts, +62 more
TL;DR: It is shown that TTNtv is the most common genetic cause of DCM in ambulant patients in the community, identify clinically important manifestations ofTTNtv-positive DCM, and define the penetrance and outcomes of TTNTV in the general population.
Book ChapterDOI

Cardiac image super-resolution with global correspondence using multi-atlas patchmatch.

TL;DR: A novel algorithm for the estimation of high-resolution cardiac MR images from single short-axis cardiac MR image stacks using an innovative super-resolution model which does not require explicit motion estimation and can be used for the reproducible estimation of 3D cardiac functional indices.