Skin lesion classification base on multi-hierarchy contrastive learning with pareto optimality

Abstract

Background:Contrastive learning has achieved remarkable success in representation learning via self-supervision in massive unlabeled data. However, as to classification tasks, there is still a certain gap between the performance of self-supervised learning and fully supervised learning. Purpose:To make the feature extractor learn the feature representation of input samples according to the induced hierarchy and enhance the performance of classifier in fully supervised setting. Methods:This study proposes a novel method, namely multi-hierarchy contrastive learning with pareto optimality (MHC-PO), to promote the overall performance of skin lesion multi-classification. In this work, we introduce a multi-hierarchy contrastive learning framework that adapts the contrastive loss to induced hierarchy structure, to enable the model to learn the feature representation via induced hierarchy. The pareto optimality method is applied to balance the common trade-off issue between the performance of feature extractor and classifier, making fully use of learned features to enhance the performance of classifiers. Results:A series of experiments were conducted on two skin lesion datasets, one is the public dataset HAM10000, and the other is the private dataset XJUSL. Experimental analysis reveals that the proposed MHC-PO surpasses the EfficientNet and other typical models in the performance of all evaluation metrics used in this study. Conclusions:The results from the experiments reflect that the proposed method enhances the clustering of the representation space and improves the overall performance of multi-classification. In addition, MHC-PO is not only promising to be applied in the medical field, but also to other domains with hierarchical data.