Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients
This work addresses the challenge of detecting hardly visible brain lesions for clinical applications like Parkinson's disease diagnosis, but it is incremental as it builds on existing auto-encoder methods.
The paper tackled the problem of detecting subtle brain anomalies in clinical settings with limited supervision by proposing two new detection criteria based on multivariate analysis of auto-encoder latent spaces, achieving favorable performance compared to supervised methods on a de novo Parkinson's disease classification task.
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.