nnOOD：自监督异常定位方法的基准框架
nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods
摘要：
医学成像中分布内和分布外的数据种类繁多，使得通用异常检测成为一项具有挑战性的任务。最近，已经开发了许多自我监督的方法，这些方法在用合成异常增强的健康数据上训练端到端模型。然而，很难比较这些方法，因为不清楚性能的提高是来自任务本身还是围绕它的训练管道，也很难评估任务是否适用于通用异常检测，因为它们通常只在有限的异常范围内进行测试。为了帮助实现这一点，我们开发了nnOOD，这是一个适应nnU-Net的框架，可以比较自监督异常定位方法。通过将合成的自我监督任务与训练过程的其余部分隔离开来，我们可以对任务进行更忠实的比较，同时也可以快速简单地对给定数据集进行评估。使用此方法，我们实现了当前最先进的任务，并在具有挑战性的X射线数据集上对其进行了评估。
英文摘要：
The wide variety of in-distribution and out-of-distribution data in medical imaging makes universal anomaly detection a challenging task. Recently a number of self-supervised methods have been developed that train end-to-end models on healthy data augmented with synthetic anomalies. However, it is difficult to compare these methods as it is not clear whether gains in performance are from the task itself or the training pipeline around it. It is also difficult to assess whether a task generalises well for universal anomaly detection, as they are often only tested on a limited range of anomalies. To assist with this we have developed nnOOD, a framework that adapts nnU-Net to allow for comparison of self-supervised anomaly localisation methods. By isolating the synthetic, self-supervised task from the rest of the training process we perform a more faithful comparison of the tasks, whilst also making the workflow for evaluating over a given dataset quick and easy. Using this we have implemented the current state-of-the-art tasks and evaluated them on a challenging X-ray dataset.
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。
