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Deep generative models for unsupervised anomaly detection in magnetic resonance imaging of the brain
Citation Link: https://doi.org/10.15480/882.16421
Publikationstyp
Doctoral Thesis
Date Issued
2026
Sprache
English
Author(s)
Advisor
Referee
Heinrich, Mattias Paul
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2025-12-04
TORE-DOI
Citation
Technische Universität Hamburg (2025)
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.
Subjects
Unsupervised Anomaly Detection
Deep Generative Models
Brain MRI
Diffusion Models
Deep Learning
DDC Class
616.07: Pathology
006.31: Machine Learning
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Behrendt_Finn_Deep-Generative-Models-for-Unsupervised-Anomaly-Detection-in-Magnetic-Resonance-Imaging-of-the-Brain.pdf
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19.62 MB
Format
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