# Improving localization-based approaches for breast cancer screening exam classification

arXiv: https://arxiv.org/abs/1908.00615 · HTML: https://mlsanity.com/paper/1908.00615/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Meta-repository of screening mammography classifiers](https://mlsanity.com/paper/2108.04800/) (2021)
- [TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detection](https://mlsanity.com/paper/2311.05192/) (2023)

## Abstract (arXiv)

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.