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arXiv · 1902.07762

Adversarial Augmentation for Enhancing Classification of Mammography Images

Lukas Jendele·Ondrej Skopek·Anton S. Becker·Ender Konukoglu
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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.

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Lukas Jendele, Ondrej Skopek, Anton S. Becker, Ender Konukoglu. 2019-02-20. Adversarial Augmentation for Enhancing Classification of Mammography Images. https://arxiv.org/abs/1902.07762

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