  Machine learning-based medical anomaly detection is an important problem that
has been extensively studied. Numerous approaches have been proposed across
various medical application domains and we observe several similarities across
these distinct applications. Despite this comparability, we observe a lack of
structured organisation of these diverse research applications such that their
advantages and limitations can be studied. The principal aim of this survey is
to provide a thorough theoretical analysis of popular deep learning techniques
in medical anomaly detection. In particular, we contribute a coherent and
systematic review of state-of-the-art techniques, comparing and contrasting
their architectural differences as well as training algorithms. Furthermore, we
provide a comprehensive overview of deep model interpretation strategies that
can be used to interpret model decisions. In addition, we outline the key
limitations of existing deep medical anomaly detection techniques and propose
key research directions for further investigation.
