Deep learning has shown to have great potential in medical applications. In critical domains as such, it is of high interest to have trustworthy algorithms which are able to tell when reliable assessments cannot be guaranteed. Detecting out-of-distribution (OOD) samples is a crucial step towards building a safe classifier. Following a previous study, showing that it is possible to classify breast cancer in point-of-care ultrasound images, this study investigates OOD detection using three different methods: softmax, energy score and deep ensembles. All methods are tested on three different OOD data sets. The results show that the energy score method outperforms the softmax method, performing well on two of the data sets. The ensemble method is the most robust, performing the best at detecting OOD samples for all three OOD data sets.

翻译：深度学习在医学应用中展现出巨大潜力。在诸如此类的关键领域，拥有能够判断何时无法保证可靠评估的可信算法至关重要。检测分布外样本是构建安全分类器的关键步骤。基于先前研究表明可在床旁超声图像中实现乳腺癌分类，本研究采用三种不同方法（softmax、能量分数和深度集成）进行分布外检测。所有方法均在三个不同分布外数据集上测试。结果显示：能量分数方法优于softmax方法，在两类数据集上表现良好；而深度集成方法最为稳健，在全部三个分布外数据集中检测分布外样本的性能最优。