A Self-Improving Skin Lesions Diagnosis Framework via Pseudo-Labeling and Self-Distillation

Abstract

In the past few years, supervised-based deep learning methods has yielded good results in skin lesions diagnosis tasks. Unfortunately, obtaining large of labels for medical images is expensive and time consuming. In this paper, we propose a self-improving skin lesions diagnosis (SISLD) framework to explore useful information in unlabeled data. We first propose a semi-supervised model ${f}$, which combining consistency and class-balanced pseudo-labeling to make full use of unlabeled data in scenarios with sparse manually labeled samples, and obtain a teacher model ${f_{t}}$ by semi-supervised self-training. Then, we introduce self-distillation method to enable knowledge distillation for the diagnosis of skin lesions. Finally, we measure diagnostic effectiveness in the context of label sparsity and class imbalance. The experiments on skin lesion images dataset ISIC2018 shows that SISLD achieves significant improvements in AUC, Accuracy, Specificity and Sensitivity.