Deep Learning for Medical Anomaly Detection -- A Survey
This survey addresses the lack of structured organization in deep learning for medical anomaly detection, which is a problem for researchers and practitioners needing to understand the advantages and limitations of existing techniques. It is an incremental contribution.
This survey paper provides a structured analysis of deep learning techniques for medical anomaly detection, identifying similarities across diverse applications. It reviews state-of-the-art methods, comparing their architectures and training algorithms, and discusses model interpretation strategies.
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.