On Out-of-Distribution Detection Algorithms with Deep Neural Skin Cancer Classifiers

Computer-aided skin cancer detection systems built with deep neural networks yield overconfident predictions on out-of-distribution examples. Motivated by the importance of out-of-distribution detection in these systems and the lack of relevant benchmarks targeted for skin cancer classification, we introduce a rich collection of out-of-distribution datasets - designed to comprehensively evaluate state-of-the-art out-of-distribution algorithms with skin cancer classifiers. In addition, we propose an adaptation in the Gram-Matrix algorithm for out-of-distribution detection that generally performs better and faster than the original algorithm for the considered skin cancer classification task. We also include a detailed discussion comparing the various state-of-the-art out-of-distribution detection algorithms and identify avenues for future research.