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The growing popularity of black box machine learning methods for medical image analysis makes their interpretability to a crucial task. To make a system, e.g. a trained neural network, trustworthy for a clinician, it needs to be able to explain its decisions and predictions. In our work we tackle the problem of explaining the predictions of medical image classifiers, trained to differentiate between different types of pathologies and healthy tissue [1].

Abstract: Interpretable Explanations of Black Box Classifiers Applied on Medical Images by Meaningful Perturbations Using Variational Autoencoders