In this paper we trained an end-to-end classifier using a deep convolutional neural network on a large data set of 8632 3D MR exams. Our model can achieve an AUC of 0.8486 in identifying malignant cases on a test set reflecting the full spectrum of the patients who undergo the breast MRI examination. We studied the effect of the data set size and the effect of using different T1-weighted images in the series on the performance of our model. This work will serve as a guideline for optimizing future deep neural networks for breast MRI interpretation.
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