Machine learning-based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …

Machine learning–based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …

Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

Anomaly explanation, also known as anomaly localization, is as important as, if not more
than, anomaly detection in many real-world applications. However, it is challenging to build …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Although deep learning has been applied to successfully address many data mining
problems, relatively limited work has been done on deep learning for anomaly detection …

Deep learning (DL) techniques have recently found success in anomaly detection (AD)
across various fields such as finance, medical services, and cloud computing. However …

A nomaly detection aims at identifying data points which are rare or significantly different
from the majority of data points. Many techniques are explored to build highly efficient and …

Deep neural networks (DNN) can achieve high performance when applied to In-Distribution
(ID) data which come from the same distribution as the training set. When presented with …

Anomaly detection is the problem of recognizing abnormal inputs based on the seen
examples of normal data. Despite recent advances of deep learning in recognizing image …