无监督病理检测：对当前最优方法的深入探讨
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art
AI总结：
本文在多个医学数据集上评估了多种前沿的无监督异常检测方法，发现新开发的特征建模方法结合自监督预训练在脑部MRI等任务中确立了新的SOTA。
摘要：
深度无监督方法在医学图像的病理检测和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型标注数据集的依赖，并且在检测任何类型的罕见病理时，比有监督的同类方法具有更强的泛化能力。随着无监督异常检测（UAD）文献的不断增长和新范式的出现，在一个通用框架中持续评估和基准测试新方法至关重要，以便重新评估当前最优（SOTA）并确定有前景的研究方向。为此，我们在多个医学数据集上评估了多种前沿的UAD方法，并将它们与脑部MRI的UAD中已确立的SOTA进行了比较。我们的实验表明，来自工业和医学文献的新开发的特征建模方法比以前的工作实现了更高的性能，并在多种模态和数据集中确立了新的SOTA。此外，我们表明此类方法能够受益于最近开发的自监督预训练算法，进一步提高其性能。最后，我们进行了一系列实验，以进一步深入了解所选模型和数据集的一些独特特征。我们的代码可在 https://github.com/iolag/UPD_study/ 找到。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
