论文标题
扫描仪不变表示的概述
Overview of Scanner Invariant Representations
论文作者
论文摘要
来自多个来源的汇总成像数据受到每个来源的偏差。对于这些扫描仪/现场偏见没有纠正的研究最多会失去统计能力，并且在最坏的情况下，他们的数据中有虚假的相关性。由于数据的匮乏，偏见效应的估计是非平凡的，因此跨站点的通信，所谓的“旅行幻影”数据，收集昂贵。然而，已经提出了许多利用直接对应关系的解决方案。与此相反，Moyer等人。 （2019年）提出了一种使用不变表示的无监督解决方案，该解决方案不需要通信，因此不需要配对图像。通过利用数据处理不平等，可以使用不变的表示形式来创建图像重建，该图像重建对其原始来源不明显，但仍然忠于基础结构。在当前摘要中，我们提供了此方法的概述。
Pooled imaging data from multiple sources is subject to bias from each source. Studies that do not correct for these scanner/site biases at best lose statistical power, and at worst leave spurious correlations in their data. Estimation of the bias effects is non-trivial due to the paucity of data with correspondence across sites, so called "traveling phantom" data, which is expensive to collect. Nevertheless, numerous solutions leveraging direct correspondence have been proposed. In contrast to this, Moyer et al. (2019) proposes an unsupervised solution using invariant representations, one which does not require correspondence and thus does not require paired images. By leveraging the data processing inequality, an invariant representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to the underlying structure. In the present abstract we provide an overview of this method.
