We investigated the use of a Bayesian deep auto-encoder to visualize intrinsic variations within a dataset in image-space. The variations were visualized by calculating a voxel-wise standard deviation over the predictions of the Bayesian deep auto-encoder. The low mutual information that was measured between the MRI and the standard deviation maps suggests that new information is contained in the standard deviation maps. This may be useful in the training of deep learning models for anomaly detection.
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