  We trained and evaluated a localization-based deep CNN for breast cancer
screening exam classification on over 200,000 exams (over 1,000,000 images).
Our model achieves an AUC of 0.919 in predicting malignancy in patients
undergoing breast cancer screening, reducing the error rate of the baseline (Wu
et al., 2019a) by 23%. In addition, the models generates bounding boxes for
benign and malignant findings, providing interpretable predictions.
