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.