# Adversarial Augmentation for Enhancing Classification of Mammography Images

Source: [Adversarial Augmentation for Enhancing Classification of Mammography Images](<https://www.sota2.com/research/paper/68fa65641e209131cea09ed7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:25:53.160Z

Authors: Lukas Jendele, Ondrej Skopek, Anton S. Becker, Ender Konukoglu

Published: 2019-02-20T20:13:24.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1902.07762>)

[PDF](<https://arxiv.org/pdf/1902.07762.pdf>)