  The increasing digitization of medical imaging enables machine learning based
improvements in detecting, visualizing and segmenting lesions, easing the
workload for medical experts. However, supervised machine learning requires
reliable labelled data, which is is often difficult or impossible to collect or
at least time consuming and thereby costly. Therefore methods requiring only
partly labeled data (semi-supervised) or no labeling at all (unsupervised
methods) have been applied more regularly. Anomaly detection is one possible
methodology that is able to leverage semi-supervised and unsupervised methods
to handle medical imaging tasks like classification and segmentation. This
paper uses a semi-exhaustive literature review of relevant anomaly detection
papers in medical imaging to cluster into applications, highlight important
results, establish lessons learned and give further advice on how to approach
anomaly detection in medical imaging. The qualitative analysis is based on
google scholar and 4 different search terms, resulting in 120 different
analysed papers. The main results showed that the current research is mostly
motivated by reducing the need for labelled data. Also, the successful and
substantial amount of research in the brain MRI domain shows the potential for
applications in further domains like OCT and chest X-ray.
