  The wide variety of in-distribution and out-of-distribution data in medical
imaging makes universal anomaly detection a challenging task. Recently a number
of self-supervised methods have been developed that train end-to-end models on
healthy data augmented with synthetic anomalies. However, it is difficult to
compare these methods as it is not clear whether gains in performance are from
the task itself or the training pipeline around it. It is also difficult to
assess whether a task generalises well for universal anomaly detection, as they
are often only tested on a limited range of anomalies. To assist with this we
have developed nnOOD, a framework that adapts nnU-Net to allow for comparison
of self-supervised anomaly localisation methods. By isolating the synthetic,
self-supervised task from the rest of the training process we perform a more
faithful comparison of the tasks, whilst also making the workflow for
evaluating over a given dataset quick and easy. Using this we have implemented
the current state-of-the-art tasks and evaluated them on a challenging X-ray
dataset.
