Out of Training Distribution Detection for Multi-Class Skin Lesion Diagnosis

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.