Machine learning–based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structured organisation of these diverse research applications such that their advantages and limitations can be studied. The principal aim of this survey is to provide a thorough theoretical analysis of popular deep learning techniques in medical anomaly detection. In particular, we contribute a coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms. Furthermore, we provide a comprehensive overview of deep model interpretation strategies that can be used to interpret model decisions. In addition, we outline the key limitations of existing deep medical anomaly detection techniques and propose key research directions for further investigation.

中文翻译：

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医学异常检测的深度学习——一项调查

基于机器学习的医学异常检测是一个已被广泛研究的重要问题。已经在各种医疗应用领域提出了许多方法，我们观察到这些不同应用之间的一些相似之处。尽管有这种可比性，但我们观察到这些不同的研究应用缺乏结构化的组织，因此可以研究它们的优点和局限性。本次调查的主要目的是对医学异常检测中流行的深度学习技术进行全面的理论分析。特别是，我们对最先进的技术进行了连贯和系统的回顾，比较和对比了它们的架构差异以及训练算法。此外，我们提供了可用于解释模型决策的深度模型解释策略的全面概述。此外，我们概述了现有深度医学异常检测技术的关键局限性，并提出了进一步研究的关键研究方向。