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Lang, D.M. ; Schwartz, E.* ; Bercea, C.-I. ; Giryes, R.* ; Schnabel, J.A.

Multispectral 3D Masked Autoencoders for Anomaly Detection in Non-Contrast Enhanced Breast MRI.

In: (Cancer Prevention Through Early Detection). Berlin [u.a.]: Springer, 2023. 55-67 (Lect. Notes Comput. Sc. ; 14295 LNCS)
DOI
Mammography is commonly used as an imaging technique in breast cancer screening but comes with the disadvantage of a high overdiagnosis rate and low sensitivity in dense tissue. dynamic contrast enhanced (DCE)-magnetic resonance imaging (MRI) features higher sensitivity but requires time consuming dynamic imaging and injection of contrast media, limiting the capability of the technique as a widespread screening method. In this work, we extend the masked autoencoder (MAE) approach to perform anomaly detection on volumetric, multispectral MRI. This new model, coined masked autoencoder for medical imaging (MAEMI), is trained on two non-contrast enhanced breast MRI sequences, aiming at lesion detection without the need for intravenous injection of contrast media and temporal image acquisition, paving the way for more widespread use of MRI in breast cancer diagnosis. During training, only non-cancerous images are presented to the model, with the purpose of localizing anomalous tumor regions during test time. We use a public dataset for model development. Performance of the architecture is evaluated in reference to subtraction images created from DCE-MRI. Code has been made publicly available: https://github.com/LangDaniel/MAEMI.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Cancer
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title Cancer Prevention Through Early Detection
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 14295 LNCS, Issue: , Pages: 55-67 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
Institute of Radiation Medicine (IRM)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Grants Helmholtz Information and Data Science Academy (HIDA) under the "Israel Exchange Program"