  Deep Neural Networks (DNNs) show a significant impact on medical imaging. One
significant problem with adopting DNNs for skin cancer classification is that
the class frequencies in the existing datasets are imbalanced. This problem
hinders the training of robust and well-generalizing models. Data Augmentation
addresses this by using existing data more effectively. However, standard data
augmentation implementations are manually designed and produce only limited
reasonably alternative data. Instead, Generative Adversarial Networks (GANs) is
utilized to generate a much broader set of augmentations. This paper proposes a
novel enhancement for the progressive generative adversarial networks (PGAN)
using self-attention mechanism. Self-attention mechanism is used to directly
model the long-range dependencies in the feature maps. Accordingly,
self-attention complements PGAN to generate fine-grained samples that comprise
clinically-meaningful information. Moreover, the stabilization technique was
applied to the enhanced generative model. To train the generative models, ISIC
2018 skin lesion challenge dataset was used to synthesize highly realistic skin
lesion samples for boosting further the classification result. We achieve an
accuracy of 70.1% which is 2.8% better than the non-augmented one of 67.3%.
