  Segmentation of skin lesions is considered as an important step in computer
aided diagnosis (CAD) for automated melanoma diagnosis. In recent years,
segmentation methods based on fully convolutional networks (FCN) have achieved
great success in general images. This success is primarily due to the
leveraging of large labelled datasets to learn features that correspond to the
shallow appearance as well as the deep semantics of the images. However, the
dependence on large dataset does not translate well into medical images. To
improve the FCN performance for skin lesion segmentations, researchers
attempted to use specific cost functions or add post-processing algorithms to
refine the coarse boundaries of the FCN results. However, the performance of
these methods is heavily reliant on the tuning of many parameters and
post-processing techniques. In this paper, we leverage the state-of-the-art
image feature learning method of generative adversarial network (GAN) for its
inherent ability to produce consistent and realistic image features by using
deep neural networks and adversarial learning concept. We improve upon GAN such
that skin lesion features can be learned at different level of complexities, in
a controlled manner. The outputs from our method is then augmented to the
existing FCN training data, thus increasing the overall feature diversity. We
evaluated our method on the ISIC 2018 skin lesion segmentation challenge
dataset and showed that it was more accurate and robust when compared to the
existing skin lesion segmentation methods.
