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