  We propose a novel unsupervised out-of-distribution detection method for
medical images based on implicit fields image representations. In our approach,
an auto-decoder feed-forward neural network learns the distribution of healthy
images in the form of a mapping between spatial coordinates and probabilities
over a proxy for tissue types. At inference time, the learnt distribution is
used to retrieve, from a given test image, a restoration, i.e. an image
maximally consistent with the input one but belonging to the healthy
distribution. Anomalies are localized using the voxel-wise probability
predicted by our model for the restored image. We tested our approach in the
task of unsupervised localization of gliomas on brain MR images and compared it
to several other VAE-based anomaly detection methods. Results show that the
proposed technique substantially outperforms them (average DICE 0.640 vs 0.518
for the best performing VAE-based alternative) while also requiring
considerably less computing time.
