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Abstract #3485

Deep learning analysis of cardiac MRI for unsupervised classification of heart disease

Carlo Biffi1,2, Ozan Oktay1, Wenjia Bai1, Giacomo Tarroni1, Antonio De Marvao2, Martin Rajchl1, Stuart Cook2,3, Declan O'Regan2, and Daniel Rueckert1

1Department of Computing, Imperial College London, London, United Kingdom, 2Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, London, United Kingdom, 3Graduate Medical School, Duke-National University of Singapore, Singapore, Singapore

Magnetic resonance imaging provides detailed assessment of cardiac structure and function. However, conventional manual phenotyping reduces the rich biological information to few global metrics. A learning-based approach providing more complex phenotypic features could offer an objective data-driven means of disease classification. In this work, we exploit a convolutional variational autoencoder model to learn low-dimensional representations of cardiac remodelling which are easily visualisable on a template shape and readily applicable in classification models. This approach yielded 91,7% accuracy in the discrimination among healthy, hypertrophic and dilated cardiomyopathy subjects, and shows promise for unsupervised classification of pathologies associated with ventricular remodelling.

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