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Bercea, C.-I. ; Wiestler, B.* ; Rueckert, D.* ; Schnabel, J.A.

Diffusion models with implicit guidance for medical anomaly detection.

In: (27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, 06-10 October 2024, Marrakesh, Morocco). Berlin [u.a.]: Springer, 2024. 211-220 (Lect. Notes Comput. Sc. ; 15011)
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Open Access Green as soon as Postprint is submitted to ZB.
Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents. Nonetheless, standard approaches may compromise critical information during pathology removal, leading to restorations that do not align with unaffected regions in the original scans. Such discrepancies can inadvertently increase false positive rates and reduce specificity, complicating radiological evaluations. This paper introduces Temporal Harmonization for Optimal Restoration (THOR), which refines the reverse diffusion process by integrating implicit guidance through intermediate masks. THOR aims to preserve the integrity of healthy tissue details in reconstructed images, ensuring fidelity to the original scan in areas unaffected by pathology. Comparative evaluations reveal that THOR surpasses existing diffusion-based methods in retaining detail and precision in image restoration and detecting and segmenting anomalies in brain MRIs and wrist X-rays. Code: https://github.com/compai-lab/2024-miccai-bercea-thor.git.
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Publication type Article: Conference contribution
Keywords Generative AI; OoD; Brain MRI; Wrist X-ray
Language english
Publication Year 2024
HGF-reported in Year 2024
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
Conference Date 06-10 October 2024
Conference Location Marrakesh, Morocco
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 15011, Issue: , Pages: 211-220 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
G-530005-001
Grants Helmholtz Association under the joint research school 'Munich School for Data Science'
EVUK program ("Next-generation Al for Integrated Diagnostics") of the Free State of Bavaria
DOI 10.1007/978-3-031-72120-5_20
WOS ID 001342238400020
Scopus ID 105007666149
Erfassungsdatum 2024-12-09
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