# Adversarial Augmentation for Enhancing Classification of Mammography Images

arXiv: https://arxiv.org/abs/1902.07762 · HTML: https://mlsanity.com/paper/1902.07762/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2019.

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

- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection](https://mlsanity.com/paper/2209.09809/) (2022)

## Abstract (arXiv)

Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging. On the example of a binary image classification task (breast cancer recognition), we show that pretraining a generative model for meaningful image augmentation helps enhance the performance of the resulting classifier. By augmenting the data, performance on downstream classification tasks could be improved even with a relatively small training set. We show that this "adversarial augmentation" yields promising results compared to classical image augmentation on the example of breast cancer classification.