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  4. Diffusion models with ensembled structure-based anomaly scoring for unsupervised anomaly detection
 
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Diffusion models with ensembled structure-based anomaly scoring for unsupervised anomaly detection

Publikationstyp
Conference Paper
Date Issued
2024-05
Sprache
English
Author(s)
Behrendt, Finn  
Medizintechnische und Intelligente Systeme E-1  
Bhattacharya, Debayan 
Medizintechnische und Intelligente Systeme E-1  
Maack, Lennart  
Medizintechnische und Intelligente Systeme E-1  
Krüger, Julia  
Opfer, Roland  
Mieling, Till Robin  
Medizintechnische und Intelligente Systeme E-1  
Schlaefer, Alexander  
Medizintechnische und Intelligente Systeme E-1  
TORE-URI
https://hdl.handle.net/11420/49107
Citation
21st IEEE International Symposium on Biomedical Imaging, ISBI 2024
Contribution to Conference
21st IEEE International Symposium on Biomedical Imaging, ISBI 2024  
Publisher DOI
10.1109/ISBI56570.2024.10635828
Scopus ID
2-s2.0-85203394703
Publisher
IEEE
ISBN
979-8-3503-1334-5
979-8-3503-1333-8
Supervised deep learning techniques show promise in medical image analysis. However, they require comprehensive annotated data sets, which poses challenges, particularly for rare diseases. Consequently, unsupervised anomaly detection (UAD) emerges as a viable alternative for pathology segmentation, as only healthy data is required for training. However, recent UAD anomaly scoring functions often focus on intensity only and neglect structural differences, which impedes the segmentation performance. This work investigates the potential of Structural Similarity (SSIM) to bridge this gap. SSIM captures both intensity and structural disparities and can be advantageous over the classical l1 error. However, we show that there is more than one optimal kernel size for the SSIM calculation for different pathologies. Therefore, we investigate an adaptive ensembling strategy for various kernel sizes to offer a more pathology-agnostic scoring mechanism. We demonstrate that this ensembling strategy can enhance the performance of DMs and mitigate the sensitivity to different kernel sizes across varying pathologies, highlighting its promise for brain MRI anomaly detection.
Subjects
Brain MRI
Diffusion Models
SSIM
Unsupervised Anomaly Detection
MLE@TUHH
DDC Class
610: Medicine, Health
Funding(s)
Vollautomatische, strukturierte Befundung von Röntgen-Thoray-Aufnahmen für die Routineanwendung in der Patientenversorgung  
TUHH
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