Deep unsupervised approaches are gathering increased attention for applications such as pathology detection and segmentation in medical images since they promise to alleviate the need for large labeled datasets and are more generalizable than their supervised counterparts in detecting any kind of rare pathology. As the Unsupervised Anomaly Detection (UAD) literature continuously grows and new paradigms emerge, it is vital to continuously evaluate and benchmark new methods in a common framework, in order to reassess the state-of-the-art (SOTA) and identify promising research directions. To this end, we evaluate a diverse selection of cutting-edge UAD methods on multiple medical datasets, comparing them against the established SOTA in UAD for brain MRI. Our experiments demonstrate that newly developed feature-modeling methods from the industrial and medical literature achieve increased performance compared to previous work and set the new SOTA in a variety of modalities and datasets. Additionally, we show that such methods are capable of benefiting from recently developed self-supervised pre-training algorithms, further increasing their performance. Finally, we perform a series of experiments in order to gain further insights into some unique characteristics of selected models and datasets. Our code can be found under https://github.com/iolag/UPD_study/.

翻译：深度无监督方法在医学图像病理检测与分割等应用中正受到越来越多的关注，因为它们有望减轻对大规模标注数据集的需求，并且在检测任何类型的罕见病理时比监督方法具有更好的泛化能力。随着无监督异常检测（UAD）文献的持续增长和新范式的涌现，在通用框架下持续评估和基准测试新方法至关重要，以便重新评估技术现状（SOTA）并识别有前景的研究方向。为此，我们在多个医学数据集上评估了多种不同的前沿UAD方法，并将其与脑部MRI的UAD既定SOTA方法进行比较。实验表明，来自工业界和医学文献的新开发特征建模方法相比以往工作取得了更高的性能，并在多种模态和数据集中设立了新的SOTA。此外，我们证明这类方法能够受益于近期发展的自监督预训练算法，进一步提升其性能。最后，我们通过一系列实验进一步深入探究了选定模型和数据集的独特特征。我们的代码可在 https://github.com/iolag/UPD_study/ 获取。