On the use of Mahalanobis distance for out-of-distribution detection
  with neural networks for medical imaging
Harry Anthony Konstantinos Kamnitsas
Implementing neural networks for clinical use in medical applications
necessitates the ability for the network to detect when input data differs
significantly from the training data, with the aim of preventing unreliable
predictions. The community has developed several methods for
out-of-distribution (OOD) detection, within which distance-based approaches -
such as Mahalanobis distance - have shown potential. This paper challenges the
prevailing community understanding that there is an optimal layer, or
combination of layers, of a neural network for applying Mahalanobis distance
for detection of any OOD pattern. Using synthetic artefacts to emulate OOD
patterns, this paper shows the optimum layer to apply Mahalanobis distance
changes with the type of OOD pattern, showing there is no one-fits-all
solution. This paper also shows that separating this OOD detector into multiple
detectors at different depths of the network can enhance the robustness for
detecting different OOD patterns. These insights were validated on real-world
OOD tasks, training models on CheXpert chest X-rays with no support devices,
then using scans with unseen pacemakers (we manually labelled 50% of CheXpert
for this research) and unseen sex as OOD cases. The results inform
best-practices for the use of Mahalanobis distance for OOD detection. The
manually annotated pacemaker labels and the project's code are available at:
https://github.com/HarryAnthony/Mahalanobis-OOD-detection.