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