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Conference Papers Year : 2023

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

Abstract

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.
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Dates and versions

hal-03998623 , version 1 (24-02-2023)

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Nicolas Pinon, Geoffroy Oudoumanessah, Robin Trombetta, Michel Dojat, Florence Forbes, et al.. Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients. ISBI 2023 - IEEE 20th International Symposium on Biomedical Imaging, Apr 2023, Cartegena de Indias, Colombia. pp.1-4. ⟨hal-03998623⟩
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