  In medical imaging, outliers can contain hypo/hyper-intensities, minor
deformations, or completely altered anatomy. To detect these irregularities it
is helpful to learn the features present in both normal and abnormal images.
However this is difficult because of the wide range of possible abnormalities
and also the number of ways that normal anatomy can vary naturally. As such, we
leverage the natural variations in normal anatomy to create a range of
synthetic abnormalities. Specifically, the same patch region is extracted from
two independent samples and replaced with an interpolation between both
patches. The interpolation factor, patch size, and patch location are randomly
sampled from uniform distributions. A wide residual encoder decoder is trained
to give a pixel-wise prediction of the patch and its interpolation factor. This
encourages the network to learn what features to expect normally and to
identify where foreign patterns have been introduced. The estimate of the
interpolation factor lends itself nicely to the derivation of an outlier score.
Meanwhile the pixel-wise output allows for pixel- and subject- level
predictions using the same model.
