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Neural network-based anomaly detection remains challenging in clinicalapplications with little or no supervised information and subtle anomalies suchas hardly visible brain lesions. Among unsupervised methods, patch-basedauto-encoders with their efficient representation power provided by theirlatent space, have shown good results for visible lesion detection. However,the commonly used reconstruction error criterion may limit their performancewhen facing less obvious lesions. In this work, we design two alternativedetection criteria. They are derived from multivariate analysis and can moredirectly capture information from latent space representations. Theirperformance compares favorably with two additional supervised learning methods,on a difficult de novo Parkinson Disease (PD) classification task.

Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients