Improving localization-based approaches for breast cancer screening exam classification

Understand

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.