  Deep unsupervised approaches are gathering increased attention for
applications such as pathology detection and segmentation in medical images
since they promise to alleviate the need for large labeled datasets and are
more generalizable than their supervised counterparts in detecting any kind of
rare pathology. As the Unsupervised Anomaly Detection (UAD) literature
continuously grows and new paradigms emerge, it is vital to continuously
evaluate and benchmark new methods in a common framework, in order to reassess
the state-of-the-art (SOTA) and identify promising research directions. To this
end, we evaluate a diverse selection of cutting-edge UAD methods on multiple
medical datasets, comparing them against the established SOTA in UAD for brain
MRI. Our experiments demonstrate that newly developed feature-modeling methods
from the industrial and medical literature achieve increased performance
compared to previous work and set the new SOTA in a variety of modalities and
datasets. Additionally, we show that such methods are capable of benefiting
from recently developed self-supervised pre-training algorithms, further
increasing their performance. Finally, we perform a series of experiments in
order to gain further insights into some unique characteristics of selected
models and datasets. Our code can be found under
https://github.com/iolag/UPD_study/.
