  Convolutional neural networks (CNNs) have achieved great success in skin
lesion classification. A balanced dataset is required to train a good model.
However, due to the appearance of different skin lesions in practice, severe or
even deadliest skin lesion types (e.g., melanoma) naturally have quite small
amount represented in a dataset. In that, classification performance
degradation occurs widely, it is significantly important to have CNNs that work
well on class imbalanced skin lesion image dataset. In this paper, we propose
SuperCon, a two-stage training strategy to overcome the class imbalance problem
on skin lesion classification. It contains two stages: (i) representation
training that tries to learn a feature representation that closely aligned
among intra-classes and distantly apart from inter-classes, and (ii) classifier
fine-tuning that aims to learn a classifier that correctly predict the label
based on the learnt representations. In the experimental evaluation, extensive
comparisons have been made among our approach and other existing approaches on
skin lesion benchmark datasets. The results show that our two-stage training
strategy effectively addresses the class imbalance classification problem, and
significantly improves existing works in terms of F1-score and AUC score,
resulting in state-of-the-art performance.
