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

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.