Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis
This addresses the need for larger datasets in medical imaging for skin cancer diagnosis, but it is incremental as it builds on existing GAN techniques.
The paper tackles the problem of limited data for skin lesion segmentation by proposing a GAN-based method to generate new lesion images using segmentation masks, which improved the mean Dice score by 5.17% on the ISBI ISIC 2017 dataset compared to classical augmentation.
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.