Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening

•We cast the challenging problem of detecting subtle brain lesions as a per voxel outlier detection problem.•Our brain anomaly detection model is trained on normal subjects only.•Our model combines unsupervised latent representation with a novel deep siamese network and one-class classification.•Pat...

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Vydané v:	Medical image analysis Ročník 60; s. 101618
Hlavní autori:	Alaverdyan, Zaruhi, Jung, Julien, Bouet, Romain, Lartizien, Carole
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 01.02.2020 Elsevier BV Elsevier
Predmet:	Adolescent Adult Aged Anomalies Anomaly detection Brain Brain lesions Brain mapping Computer Science Data analysis Datasets as Topic Deep learning Epilepsy Epilepsy - diagnostic imaging Feature extraction Female Heterogeneity Humans Image Interpretation, Computer-Assisted - methods Lesions Localization Machine Learning Magnetic Resonance Imaging Male Medical Imaging Middle Aged Neural networks Neural Networks, Computer Neuroimaging Outliers (statistics) Regularization Regularized siamese network Representations Signal and Image Processing Spatial discrimination Unsupervised Machine Learning Unsupervised representation learning Wasserstein autoencoder Wasserstein autoencoder Deep learning Brain lesions Anomaly detection Regularized siamese network Unsupervised representation learning Regularized Siamese network Deep Learning
ISSN:	1361-8415, 1361-8423, 1361-8423
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