Deep Learning for Medical Anomaly Detection – A Survey

By Tharindu Fernando, Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes. ACM Computing Surveys, 2020

426 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4ed4dc4df5dbde2677852c33080e3c893bf0a5ba

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection (2023, neutral): “…2020; Pang et al., 2021a; Reiss & Hoshen, 2023; You et al., 2022; Liznerski et al., 2020; Ding et al., 2022; Zhou et al., 2023; Cao et al., 2023) and medical image analysis (Pang et al., 2021a; Qin et al., 2022; Liu et al., 2023; Ding et al., 2022; Tian et al., 2021; 2023; Fernando et al., 2021).”
A Survey on Anomaly Detection for Technical Systems using LSTM Networks (2021, background): “Survey studies regarding such deep learning approaches for anomaly detection have been conducted in recent years: In [5], advantages and disadvantages as well as computational complexity of (semi-)supervised, unsupervised and hybrid deep learning approaches are described.”
Diffusion models in medical imaging: A comprehensive survey (2022, background): “Medical anomaly detection is an important topic in computer vision, aiming to highlight the anomalous regions of the image [128, 129, 130].”
A Unified Model for Multi-class Anomaly Detection (2022, background): “Semantic AD [1, 4] also involves multiple classes, which mainly focus on anomaly detection extended from classification datasets ( e.g. , CIFAR-10 CIFAR-10 First, we focus more on the industrial anomaly detection dataset, MVTec-AD [2], which is of more practical usage.”
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies (2023, background): “Visual anomaly detection is a rapidly growing area of research with a diverse range of applications, including medical imaging [3, 18, 36], autonomous driving [13, 23, 30], and industrial inspection [7, 17, 40].”