ReFixMatch-LS: reusing pseudo-labels for semi-supervised skin lesion classification

Recent research on semi-supervised learning (SSL) is mainly based on the method of consistency regularization, which relies on domain-specific data augmentation. Pseudo-labeling is a more general method that has no such restrictions but performs limited by noisy training. We combine both approaches and focus on generating pseudo-labels using domain-independent weak augmentation. In this article, we propose ReFixMatch-LS and apply it to the classification of medical images. First, we reduce the impact of noisy artificial labels by label smoothing and consistent regularization. Then, by recording high-confidence pseudo-labels generated from each epoch during training, we reuse the generated pseudo-labels to train the model in the subsequent epochs. ReFixMatch-LS effectively increases the number of pseudo-labels and improves the model performance. We validate the effectiveness of ReFixMatch-LS on skin lesion diagnosis in the ISIC 2018 and ISIC 2019 challenge datasets, obtaining AUCs of 91.54%, 93.68%, 94.55%, and 95.47% on the four proportions of labeled data from ISIC 2018.

Automated summary

In this article, we propose a semi-supervised learning method called ReFixMatch-LS, which combines consistency regularization and pseudo-labeling. We apply this method to medical image classification and reduce the impact of noisy labels through label smoothing and consistency regularization. By recording high-confidence pseudo-labels generated during training, we effectively increase the number of pseudo-labels and improve the model performance.