TY - GEN
T1 - Multispectral 3D Masked Autoencoders for Anomaly Detection in Non-Contrast Enhanced Breast MRI
AU - Lang, Daniel M.
AU - Schwartz, Eli
AU - Bercea, Cosmin I.
AU - Giryes, Raja
AU - Schnabel, Julia A.
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85175949421&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-45350-2_5
DO - 10.1007/978-3-031-45350-2_5
M3 - Conference contribution
AN - SCOPUS:85175949421
SN - 9783031453496
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 55
EP - 67
BT - Cancer Prevention Through Early Detection - 2nd International Workshop, CaPTion 2023, Held in Conjunction with MICCAI 2023, Proceedings
A2 - Ali, Sharib
A2 - van der Sommen, Fons
A2 - van Eijnatten, Maureen
A2 - Kolenbrander, Iris
A2 - Papież, Bartłomiej W.
A2 - Jin, Yueming
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Workshop on Cancer Prevention through early detecTion, CaPTion 2023
Y2 - 12 October 2023 through 12 October 2023
ER -
