Adversarial Augmentation for Enhancing Classification of Mammography Images

Understand

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.