Deep generative models for unsupervised anomaly detection in magnetic resonance imaging of the brain
Abstract
This thesis explores unsupervised anomaly detection in magnetic resonance imaging of the brain, leveraging generative models to model the distribution of healthy anatomy and identify deviations as anomalies. We examine the integration of additional contextual information into generative models and anomaly scoring methods. Our approaches enhance reconstruction quality and anomaly detection accuracy.
Fields of Study & Subjects
Anomaly Detection Techniques and Applications
Medical Image Segmentation Techniques
Advanced MRI Techniques and Applications
Publication details
- Publication date
- 1 January 2026
- Language
- EN
- Document type
- Thesis