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Realistic Adversarial Data Augmentation for MR Image Segmentation
[article]
2020
arXiv
pre-print
Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learning. Acquiring and manually labelling a large medical dataset is expensive and sometimes impractical due to data sharing and privacy issues. In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks,
arXiv:2006.13322v1
fatcat:3kbkfjurajgpjeqgl57ki5b24m