MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection

Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-based and reconstruction error-based techniques. Howev...

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Bibliographic Details
Published in:	IEEE transactions on medical imaging Vol. 40; no. 3; pp. 1032 - 1041
Main Authors:	Chen, Yurong, Zhang, Hui, Wang, Yaonan, Yang, Yimin, Zhou, Xianen, Wu, Q. M. Jonathan
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.03.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Abnormalities Algorithms Anomalies Anomaly detection Attention attention mechanism Brain - diagnostic imaging Computed tomography Convolution COVID-19 COVID-19 - diagnostic imaging COVID-19 diagnose Datasets Decision making Environmental information Error detection Feature extraction hash coding Humans Image Interpretation, Computer-Assisted - methods Image reconstruction Inference Lung - diagnostic imaging Magnetic Resonance Imaging Memory memory autoencoder Memory modules Neural Networks, Computer Object recognition Outliers (statistics) Receptive field Reconstruction SARS-CoV-2 Tomography, X-Ray Computed Training Visual perception
ISSN:	0278-0062, 1558-254X, 1558-254X
Online Access:	Get full text
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