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

Zara Alaverdyan, Julien Jung, Romain Bouet, Carole Lartizien

Abstract:

Computer aided diagnosis (CAD) systems are designed to assist clinicians in various
tasks, including highlighting abnormal regions in medical images. Common
methods exploit supervised learning using annotated data sets and perform classification
at voxel-level. However, many pathologies are characterized by subtle
lesions that may be located anywhere in the organ of interest, have various shapes,
sizes and textures. Acquiring a data set adequately representing the heterogeneity
of such pathologies is therefore a major issue. Moreover, when a lesion is not
visually detected on a scan, outlining it accurately is not feasible. Performing
supervised learning on such labeled data would not be reliable. In this study, we
consider the problem of detecting subtle epilepsy lesions in multiparametric (T1w,
FLAIR) MRI exams considered as normal (MRI-negative). We cast this problem
as an outlier detection problem and build on a previously proposed approach that
consists in learning a oc-SVM model for each voxel in the brain volume using a
small number of clinically-guided features. Our goal in this study is to make
a step forward by replacing the handcrafted features with automatically learnt
representations using neural networks. We propose a novel version of siamese networks
trained on patches extracted from healthy patients’ scans only. This network,
composed of stacked convolutional autoencoders as subnetworks, is regularized by
the reconstruction error of the patches. It is designed to map patches centered at
the same spatial localization to ’close’ representations with respect to the chosen
metric (i.e. cosine) in a latent space. Finally, the middle layer representations of
the subnetworks are fed into oc-SVM models at voxel-level. The model is trained on
75 healthy subjects and validated on 21 patients with confirmed epilepsy lesions
(with 18 MR negative patients) and shows a promising performance.

Author Affiliation: Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, Lyon, France

Keywords: Epilepsy, Siamese network, Outlier detection, Anomaly detection, Deep learning