Semi-supervised Learning Regularized by Adversarial Perturbation and Diversity Maximization

Abstract

In many clinical settings, a lot of medical image datasets suffer from the imbalance problem, which makes the predictions of the trained models to be biased toward majority classes. Semi-supervised Learning (SSL) algorithms trained with such imbalanced datasets become more problematic since pseudo-labels of unlabeled data are generated from the model’s biased predictions. Towards addressing this challenge, we propose a SSL framework which can effectively leverage unlabeled data for improving the performance of deep convolutional neural networks. It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under adversarial perturbation and diversity maximization. We additionally propose to use uncertainty estimation to filter out low-quality consistency targets for the unlabeled data. We conduct comprehensive experiments to evaluate the performance of our method on two publicly available datasets, i.e., the ISIC 2018 challenge dataset for skin lesion classification and the ChestX-ray14 dataset for thorax disease classification. The experimental results demonstrated the efficacy of the present method.