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Segmentation of skin lesions is considered as an important step in computeraided diagnosis (CAD) for automated melanoma diagnosis. In recent years,segmentation methods based on fully convolutional networks (FCN) have achievedgreat success in general images. This success is primarily due to theleveraging of large labelled datasets to learn features that correspond to theshallow appearance as well as the deep semantics of the images. However, thedependence on large dataset does not translate well into medical images. Toimprove the FCN performance for skin lesion segmentations, researchersattempted to use specific cost functions or add post-processing algorithms torefine the coarse boundaries of the FCN results. However, the performance ofthese methods is heavily reliant on the tuning of many parameters andpost-processing techniques. In this paper, we leverage the state-of-the-artimage feature learning method of generative adversarial network (GAN) for itsinherent ability to produce consistent and realistic image features by usingdeep neural networks and adversarial learning concept. We improve upon GAN suchthat skin lesion features can be learned at different level of complexities, ina controlled manner. The outputs from our method is then augmented to theexisting FCN training data, thus increasing the overall feature diversity. Weevaluated our method on the ISIC 2018 skin lesion segmentation challengedataset and showed that it was more accurate and robust when compared to theexisting skin lesion segmentation methods.

Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation