  Skin lesion segmentation is a vital task in skin cancer diagnosis and further
treatment. Although deep learning based approaches have significantly improved
the segmentation accuracy, these algorithms are still reliant on having a large
enough dataset in order to achieve adequate results. Inspired by the immense
success of generative adversarial networks (GANs), we propose a GAN-based
augmentation of the original dataset in order to improve the segmentation
performance. In particular, we use the segmentation masks available in the
training dataset to train the Mask2Lesion model, and use the model to generate
new lesion images given any arbitrary mask, which are then used to augment the
original training dataset. We test Mask2Lesion augmentation on the ISBI ISIC
2017 Skin Lesion Segmentation Challenge dataset and achieve an improvement of
5.17% in the mean Dice score as compared to a model trained with only classical
data augmentation techniques.
