Semi-supervised medical image classification based on CamMix

Collecting a large amount of labeled data is crutial for training deep neural network, which is a limitation for medical image classification because it necessarily involves expert knowledge. To mitigate this problem of insufficient labeled medical data, in this work, we propose a novel semi-supervised framework for medical image classification. For unlabeled data, we apply the consistency-based strategy to produce high-quality pseudo label, which encourages model to output the same predictions under different perturbations. In addition, we present a novel mixed sample data augmentation CamMix to effectively exploit the relation between samples, mixing pairs of input data and labels according to the class activation map mask. We have evaluated our proposed method on two public medical image datasets, interstitial lung disease dataset and ISIC 2018 skin lesion analysis dataset. The results demonstrate superior performance of our method over other existing methods on the two datasets. Meanwhile, our proposed CamMix performs better than the current mixed sample data augmentation methods.