  Melanoma is one of the ten most common cancers in the US. Early detection is
crucial for survival, but often the cancer is diagnosed in the fatal stage.
Deep learning has the potential to improve cancer detection rates, but its
applicability to melanoma detection is compromised by the limitations of the
available skin lesion databases, which are small, heavily imbalanced, and
contain images with occlusions. We build deep-learning-based tools for data
purification and augmentation to counter-act these limitations. The developed
tools can be utilized in a deep learning system for lesion classification and
we show how to build such a system. The system heavily relies on the processing
unit for removing image occlusions and the data generation unit, based on
generative adversarial networks, for populating scarce lesion classes, or
equivalently creating virtual patients with pre-defined types of lesions. We
empirically verify our approach and show that incorporating these two units
into melanoma detection system results in the superior performance over common
baselines.
