Abstract:
Recent years have seen significant advances in automated diagnosis systems for medical imaging tasks aimed to support the decision-making process. More specifically, Conv...Show MoreMetadata
Abstract:
Recent years have seen significant advances in automated diagnosis systems for medical imaging tasks aimed to support the decision-making process. More specifically, Convolutional neural networks (CNN) show remarkable performance in tasks such as multi-class skin lesion classification using images. However, concerns remain about the deployment of such models, as real-world test data distribution can significantly differ from the distribution of the training data. In other words, models can classify unknown samples as known classes with high confidence, which could lead to catastrophic mistakes. In line with these concerns, this paper focuses on accessing the current methods to detect out-of-training distribution samples in the context of skin lesion classification. The results contribute towards the understanding of the effectiveness of out-of-distribution detection methods.
Published in: 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)
Date of Conference: 25-27 August 2021
Date Added to IEEE Xplore: 30 September 2021
ISBN Information: