Advances in deep learning (DL) have resulted in impressive accuracy in some medical image classification tasks, but often deep models lack interpretability . For many problems in medicine there is a wealth of existing clinical knowledge to draw upon, which may be useful in generating explanations, but it is not obvious how this knowledge can be encoded into DL models . We propose a novel DL framework for image-based classification based on a variational autoencoder (VAE) The VAE disentangles the latent space based on ‘explanations’ drawn from clinical knowledge . The framework can predict outputs as well as explanations for these outputs, and also raises the possibility of discovering new biomarkers that are separate (or disentangled) from the existing knowledge . We demonstrate our framework on the problem of predicting response of patients with cardiomyopathy to cardiac resynchronization therapy (CRT) from cine cardiac magnetic resonance images . The sensitivity and specificity of the proposed model is 88.43% and 84.39% respectively, and we showcase the potential of our model in enhancing understanding of the factors contributing to CRT response . We showcase the . potential of the model in finding new biomarker that is separate ( or disentangling) from . existing . existing biomarkers, respectively, to the CRT . The potential of this model in

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Keywords : model - existing - knowledge - explanations - models -

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