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

Author:

Alaverdyan Zaruhi,Jung Julien,Bouet Romain,Lartizien CaroleORCID

Funder

Université de Lyon

French National Research Agency

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference73 articles.

1. Cortical feature analysis and machine learning improves detection of “MRI-negative” focal cortical dysplasia;Ahmed;Epilepsy Behav.,2015

2. Decrypting “cryptogenic” epilepsy: semi-supervised hierarchical conditional random fields for detecting cortical lesions in MRI-negative patients;Ahmed;J. Mach. Learn. Res.,2016

3. Is it worth pursuing surgery for epilepsy in patients with normal neuroimaging?;Alarcon;J. Neurol Neurosurg. Psychiatry,2006

4. Unsupervised feature learning for outlier detection with stacked convolutional autoencoders, siamese networks and wasserstein autoencoders: application to epilepsy detection;Alaverdyan,2018

5. Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: application to epilepsy lesion screening;Alaverdyan,2018

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