Abstract
An assumption-free automatic check of medical images for potentially overseen
anomalies would be a valuable assistance for a radiologist. Deep learning and
especially Variational Auto-Encoders (VAEs) have shown great potential in the
unsupervised learning of data distributions. In principle, this allows for such
a check and even the localization of parts in the image that are most
suspicious. Currently, however, the reconstruction-based localization by design
requires adjusting the model architecture to the specific problem looked at
during evaluation. This contradicts the principle of building assumption-free
models. We propose complementing the localization part with a term derived from
the Kullback-Leibler (KL)-divergence. For validation, we perform a series of
experiments on FashionMNIST as well as on a medical task including >1000
healthy and >250 brain tumor patients. Results show that the proposed formalism
outperforms the state of the art VAE-based localization of anomalies across
many hyperparameter settings and also shows a competitive max performance.
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