Hierarchy-aware contrastive learning with late fusion for skin lesion classification

The incidence rate of skin cancers is increasing worldwide annually. Using machine learning and deep learning for skin lesion classification is one of the essential research topics. In this study, we formulate a major-type misclassification problem that previous studies did not consider in the multi-class skin lesion classification. Moreover, addressing the major-type misclassification problem is significant for real-world computer-aided diagnosis.

This study presents a novel method, namely Hierarchy-Aware Contrastive Learning with Late Fusion (HAC-LF), to improve the overall performance of multi-class skin classification. In HAC-LF, we design a new loss function, Hierarchy-Aware Contrastive Loss (HAC Loss), to reduce the impact of the major-type misclassification problem. The late fusion method is applied to balance the major-type and multi-class classification performance.

We conduct a series of experiments with the ISIC 2019 Challenges dataset, which consists of three skin lesion datasets, to verify the performance of our methods. The results show that our proposed method surpasses the representative deep learning methods for skin lesion classification in all evaluation metrics used in this study. HAC-LF achieves 0.871, 0.842, 0.889 for accuracy, sensitivity, and specificity in the major-type classification, respectively. With the imbalanced class distribution, HAC-LF outperforms the baseline model regarding the sensitivity of minority classes.

This research formulates a major-type misclassification problem. We propose HAC-LF to deal with it and boost the multi-class skin lesion classification performance. According to the results, the advantage of HAC-LF is that the proposed HAC Loss can beneficially reduce the impact of the major-type misclassification by decreasing the major-type error rate. Besides the medical field HAC-LF is promising to be applied to other domains possessing the data with the hierarchical structure.