  There are limited works showing the efficacy of unsupervised
Out-of-Distribution (OOD) methods on complex medical data. Here, we present
preliminary findings of our unsupervised OOD detection algorithm, SimCLR-LOF,
as well as a recent state of the art approach (SSD), applied on medical images.
SimCLR-LOF learns semantically meaningful features using SimCLR and uses LOF
for scoring if a test sample is OOD. We evaluated on the multi-source
International Skin Imaging Collaboration (ISIC) 2019 dataset, and show results
that are competitive with SSD as well as with recent supervised approaches
applied on the same data.
