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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Veröffentlicht in:IEEE transactions on medical imaging Jg. 40; H. 3; S. 1032 - 1041
Hauptverfasser: Chen, Yurong, Zhang, Hui, Wang, Yaonan, Yang, Yimin, Zhou, Xianen, Wu, Q. M. Jonathan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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. However, due to the bottleneck of defining encompasses of real-world high-diversity outliers and inaccessible inference process, individually, most of them have not derived groundbreaking progress. To deal with those imperfectness, and motivated by memory-based decision-making and visual attention mechanism as a filter to select environmental information in human vision perceptual system, in this paper, we propose a Multi-scale Attention Memory with hash addressing Autoencoder network (MAMA Net) for anomaly detection. First, to overcome a battery of problems result from the restricted stationary receptive field of convolution operator, we coin the multi-scale global spatial attention block which can be straightforwardly plugged into any networks as sampling, upsampling and downsampling function. On account of its efficient features representation ability, networks can achieve competitive results with only several level blocks. Second, it's observed that traditional autoencoder can only learn an ambiguous model that also reconstructs anomalies "well" due to lack of constraints in training and inference process. To mitigate this challenge, we design a hash addressing memory module that proves abnormalities to produce higher reconstruction error for classification. In addition, we couple the mean square error (MSE) with Wasserstein loss to improve the encoding data distribution. Experiments on various datasets, including two different COVID-19 datasets and one brain MRI (RIDER) dataset prove the robustness and excellent generalization of the proposed MAMA Net.
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2020.3045295