Adversarial Augmentation for Enhancing Classification of Mammography Images

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.

References in corpus (5)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Assessing Generative Models via Precision and Recall
• Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks
• Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize
• Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks