IEEE J Biomed Health Inform. 2023 Jan;27(1):157-165. doi: 10.1109/JBHI.2022.3171582. Epub 2023 Jan 4.
Deep learning methods have shown outstanding potential in dermatology for skin lesion detection and identification. However, they usually require annotations beforehand and can only classify lesion classes seen in the training set. Moreover, large-scale, open-sourced medical datasets normally have far fewer annotated classes than in real life, further aggravating the problem. This paper proposes a novel method called DNF-OOD, which applies a non-parametric deep forest-based approach to the problem of out-of-distribution (OOD) detection. By leveraging a maximum probabilistic routing strategy and over-confidence penalty term, the proposed method can achieve better performance on the task of detecting OOD skin lesion images, which is challenging due to the large intra-class variability in such images. We evaluate our OOD detection method on images from two large, publicly-available skin lesion datasets, ISIC2019 and DermNet, and compare it against recently-proposed approaches. Results demonstrate the potential of our DNF-OOD framework for detecting OOD skin images.
深度学习方法在皮肤病学中的皮肤损伤检测和识别方面显示出了卓越的潜力。然而，它们通常需要事先进行标注，并且只能对训练集中出现的损伤类别进行分类。此外，大规模的开源医疗数据集通常只有比实际情况少得多的标注类别，进一步加剧了这个问题。本文提出了一种名为 DNF-OOD 的新方法，该方法将一种基于非参数深度森林的方法应用于分布外（OOD）检测问题。通过利用最大概率路由策略和过度自信惩罚项，所提出的方法可以在检测 OOD 皮肤损伤图像的任务中实现更好的性能，由于这些图像中存在较大的类内可变性，因此该任务具有挑战性。我们在两个大型的公开皮肤损伤数据集 ISIC2019 和 DermNet 上评估了我们的 OOD 检测方法，并与最近提出的方法进行了比较。结果表明，我们的 DNF-OOD 框架在检测 OOD 皮肤图像方面具有潜力。
