论文标题
对脑MRI无监督异常检测的代表性特性的研究
A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRI
论文作者
论文摘要
MRI中的异常检测在成像和诊断中具有很高的临床价值。无监督的异常检测方法提供了基于重建或潜在嵌入的有趣制剂，提供了一种观察与分解相关的属性的方法。我们研究了四种现有的建模方法，并使用简单的数据科学工具报告了我们的经验观察结果，以从分解的角度寻求结果，因为这与无监督无监督的异常检测的任务最相关，考虑到大脑结构MRI的情况。我们的研究表明，表现出与分解特性相关特性的异常检测算法具有很好的电容性，并具有区分正常数据和异常数据的差异能力。我们已经在多个异常和正常数据集中验证了我们的观察结果。
Anomaly detection in MRI is of high clinical value in imaging and diagnosis. Unsupervised methods for anomaly detection provide interesting formulations based on reconstruction or latent embedding, offering a way to observe properties related to factorization. We study four existing modeling methods, and report our empirical observations using simple data science tools, to seek outcomes from the perspective of factorization as it would be most relevant to the task of unsupervised anomaly detection, considering the case of brain structural MRI. Our study indicates that anomaly detection algorithms that exhibit factorization related properties are well capacitated with delineatory capabilities to distinguish between normal and anomaly data. We have validated our observations in multiple anomaly and normal datasets.
