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

Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection.

In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2023). Berlin [u.a.]: Springer, 2023. 293-303 (Lect. Notes Comput. Sc. ; 14224 LNCS)
DOI
Early and accurate disease detection is crucial for patient management and successful treatment outcomes. However, the automatic identification of anomalies in medical images can be challenging. Conventional methods rely on large labeled datasets which are difficult to obtain. To overcome these limitations, we introduce a novel unsupervised approach, called PHANES (Pseudo Healthy generative networks for ANomaly Segmentation). Our method has the capability of reversing anomalies, i.e., preserving healthy tissue and replacing anomalous regions with pseudo-healthy (PH) reconstructions. Unlike recent diffusion models, our method does not rely on a learned noise distribution nor does it introduce random alterations to the entire image. Instead, we use latent generative networks to create masks around possible anomalies, which are refined using inpainting generative networks. We demonstrate the effectiveness of PHANES in detecting stroke lesions in T1w brain MRI datasets and show significant improvements over state-of-the-art (SOTA) methods. We believe that our proposed framework will open new avenues for interpretable, fast, and accurate anomaly segmentation with the potential to support various clinical-oriented downstream tasks. Code: https://github.com/ci-ber/PHANES
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Generative Networks ; Unsupervised Anomaly Detection
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Medical Image Computing and Computer Assisted Intervention – MICCAI 2023
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 14224 LNCS, Heft: , Seiten: 293-303 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
G-530005-001
Förderungen Helmholtz Association under the joint research school "Munich School for Data Science -MUDS"
DOI 10.1007/978-3-031-43904-9_29
WOS ID 001109633700029
Scopus ID 85174691721
Erfassungsdatum 2023-11-28
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