Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification

Marc Combalia, Ferran Hueto, Susana Puig, Josep Malvehy, Veronica Vilaplana; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 744-745

Abstract


The high performance of machine learning algorithms for the task of skin lesion classification has been proven over the past few years. However, real-world implementations are still scarce. One of the reasons could be that most methods do not quantify the uncertainty in the predictions and are not able to detect data that is anomalous or significantly different from that used in training, which may lead to a lack of confidence in the automated diagnosis or errors in the interpretation of results. In this work, we explore the use of uncertainty estimation techniques and metrics for deep neural networks based on Monte-Carlo sampling and apply them to the problem of skin lesion classification on data from ISIC Challenges 2018 and 2019. Our results show that uncertainty metrics can be successfully used to detect difficult and out-of-distribution samples.

Related Material


[pdf]
[bibtex]
@InProceedings{Combalia_2020_CVPR_Workshops,
author = {Combalia, Marc and Hueto, Ferran and Puig, Susana and Malvehy, Josep and Vilaplana, Veronica},
title = {Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}