Detection and prediction of background parenchymal enhancement on breast MRI using deep learning
Badhan Kumar Das1,2, Lorenz A. Kapsner1, Sabine Ohlmeyer1, Frederik B. Laun1, Andreas Maier2, Michael Uder1, Evelyn Wenkel1, Sebastian Bickelhaupt1, and Andrzej Liebert1
1Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany, 2Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
The purpose of this work was to automatically classify BPE using T1-weighted subtraction volumes and diffusion-weighted imaging volumes in breast MRI. The dataset consisted of 621 routine breast MRI examination acquired at University Hospital Erlangen. 2D MIP and 3D T1-subtraction volumes were used for the automatic detection of BPE classes. Multi-b-value DWI (up to1500s/mm2) DWI images were used for automatic prediction. ResNet and DenseNet models were used for 2D and 3D data respectively. The study demonstrated an AUROC of 0.8107 on the test set using the T1-subtraction volumes. With DWI volumes, a slightly decreased AuROC of 0.78 was achieved.
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