Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis
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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 generate new synthetic skin lesion images using a conditional GAN, modeling this as a paired image-to-image translation task, 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 observe that it improves the segmentation accuracy, compared to a model trained with only classical data augmentation techniques by 1.12
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