  Deep unsupervised representation learning has recently led to new approaches
in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main
principle behind these works is to learn a model of normal anatomy by learning
to compress and recover healthy data. This allows to spot abnormal structures
from erroneous recoveries of compressed, potentially anomalous samples. The
concept is of great interest to the medical image analysis community as it i)
relieves from the need of vast amounts of manually segmented training data---a
necessity for and pitfall of current supervised Deep Learning---and ii)
theoretically allows to detect arbitrary, even rare pathologies which
supervised approaches might fail to find. To date, the experimental design of
most works hinders a valid comparison, because i) they are evaluated against
different datasets and different pathologies, ii) use different image
resolutions and iii) different model architectures with varying complexity. The
intent of this work is to establish comparability among recent methods by
utilizing a single architecture, a single resolution and the same dataset(s).
Besides providing a ranking of the methods, we also try to answer questions
like i) how many healthy training subjects are needed to model normality and
ii) if the reviewed approaches are also sensitive to domain shift. Further, we
identify open challenges and provide suggestions for future community efforts
and research directions.
