  Skin lesion datasets consist predominantly of normal samples with only a
small percentage of abnormal ones, giving rise to the class imbalance problem.
Also, skin lesion images are largely similar in overall appearance owing to the
low inter-class variability. In this paper, we propose a two-stage framework
for automatic classification of skin lesion images using adversarial training
and transfer learning toward melanoma detection. In the first stage, we
leverage the inter-class variation of the data distribution for the task of
conditional image synthesis by learning the inter-class mapping and
synthesizing under-represented class samples from the over-represented ones
using unpaired image-to-image translation. In the second stage, we train a deep
convolutional neural network for skin lesion classification using the original
training set combined with the newly synthesized under-represented class
samples. The training of this classifier is carried out by minimizing the focal
loss function, which assists the model in learning from hard examples, while
down-weighting the easy ones. Experiments conducted on a dermatology image
benchmark demonstrate the superiority of our proposed approach over several
standard baseline methods, achieving significant performance improvements.
Interestingly, we show through feature visualization and analysis that our
method leads to context based lesion assessment that can reach an expert
dermatologist level.
