We present the initial results of two unsupervised out-of-distribution (OOD) detection algorithms, designed to flag dermoscopic images of lesions from classes not seen during training. When evaluated on the ISIC 2019 dataset - using 6 classes as in-distribution and 2 as OOD - the scores from our algorithms produced AUROC’s of 0.694/0.642. The images in ISIC 2019 mainly come from two datasets - HAM and BCN. When restricting our evaluation to consider only images from HAM the AUROC was 0.758/0.765, and when considering the images from BCN only, the AUROC dropped to 0.645/0.504.
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