Class-Center Involved Triplet Loss for Skin Disease Classification on Imbalanced Data

It is ideal to develop intelligent systems to accurately di- agnose diseases as human specialists. However, due to the highly imbalanced data problem between common and rare diseases, it is still an open problem for the systems to ef- fectively learn to recognize both common and rare diseases. We propose utilizing triplet modelling to overcome the data imbalance issue for the rare diseases. Moreover, we further develop a class-center based triplet loss in order to make the triplet-based learning more stable. Extensive evaluation on two skin image classification tasks shows that the triplet- based approach is very effective and outperforms the widely used methods for solving the imbalance problem, including oversampling, class weighting, and using focal loss.