Semi-Supervised Learning with Online Knowledge Distillation for Skin Lesion Classification

TL;DR

This paper presents a semi-supervised deep learning method combining ensemble learning and online knowledge distillation to improve skin lesion classification, reducing labeled data requirements and enhancing model performance in resource-limited settings.

Contribution

Introduces a novel semi-supervised approach that integrates ensemble learning with online knowledge distillation for skin lesion classification, achieving superior results with less labeled data.

Findings

Outperforms state-of-the-art on benchmark datasets

Models trained with this method perform better than independently trained models

Reduces labeled data needs while maintaining high accuracy

Abstract

Deep Learning has emerged as a promising approach for skin lesion analysis. However, existing methods mostly rely on fully supervised learning, requiring extensive labeled data, which is challenging and costly to obtain. To alleviate this annotation burden, this study introduces a novel semi-supervised deep learning approach that integrates ensemble learning with online knowledge distillation for enhanced skin lesion classification. Our methodology involves training an ensemble of convolutional neural network models, using online knowledge distillation to transfer insights from the ensemble to its members. This process aims to enhance the performance of each model within the ensemble, thereby elevating the overall performance of the ensemble itself. Post-training, any individual model within the ensemble can be deployed at test time, as each member is trained to deliver comparable performance to the ensemble. This is particularly beneficial in resource-constrained environments. Experimental results demonstrate that the knowledge-distilled individual model performs better than independently trained models. Our approach demonstrates superior performance on both the \emph{International Skin Imaging Collaboration} 2018 and 2019 public benchmark datasets, surpassing current state-of-the-art results. By leveraging ensemble learning and online knowledge distillation, our method reduces the need for extensive labeled data while providing a more resource-efficient solution for skin lesion classification in real-world scenarios.