There are limited works showing the efficacy of unsupervised Out-of-Distribution (OOD) methods on complex medical data. Here, we present preliminary findings of our unsupervised OOD detection algorithm, SimCLR-LOF, as well as a recent state of the art approach (SSD), applied on medical images. SimCLR-LOF learns semantically meaningful features using SimCLR and uses LOF for scoring if a test sample is OOD. We evaluated on the multi-source International Skin Imaging Collaboration (ISIC) 2019 dataset, and show results that are competitive with SSD as well as with recent supervised approaches applied on the same data.

翻译：显示未经监督的分发方法在复杂医疗数据方面的功效的作品有限。 在这里,我们介绍了未经监督的 OOD 检测算法(SimCLR-LOF)的初步结果,以及医疗图像应用的最新先进方法(SSD)。 SimCLR-LOF 学会了使用SimCLR(SISD)具有内在意义的特征,如果测试样品是OOD(OOD),则使用LOF(LOF)来评分。我们用多来源国际皮肤成像协作(ISIC) (ISIC) (ISIC) (ISIC) 2019) 数据集进行了评估,并展示了与SD(ISD) 以及最近对同一数据应用的监督方法具有竞争力的结果。