On Out-of-Distribution Detection Algorithms with Deep Neural Skin Cancer Classifiers
Abstract
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.
Cite
Text
Pacheco et al. "On Out-of-Distribution Detection Algorithms with Deep Neural Skin Cancer Classifiers." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00374Markdown
[Pacheco et al. "On Out-of-Distribution Detection Algorithms with Deep Neural Skin Cancer Classifiers." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/pacheco2020cvprw-outofdistribution/) doi:10.1109/CVPRW50498.2020.00374BibTeX
@inproceedings{pacheco2020cvprw-outofdistribution,
title = {{On Out-of-Distribution Detection Algorithms with Deep Neural Skin Cancer Classifiers}},
author = {Pacheco, André G. C. and Sastry, Chandramouli Shama and Trappenberg, Thomas and Oore, Sageev and Krohling, Renato A.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2020},
pages = {3152-3161},
doi = {10.1109/CVPRW50498.2020.00374},
url = {https://mlanthology.org/cvprw/2020/pacheco2020cvprw-outofdistribution/}
}