Growth threshold for pseudo labeling and pseudo label dropout for semi-supervised medical image classification

Semi-supervised learning (SSL) provides methods to improve model performance through unlabeled samples. In medical image analysis, the challenges of multi-category classification and imbalance learning must be addressed effectively. Pseudo labeling is not specifically designed for multi-category and category imbalance problems. In this paper, we propose the Growth Threshold for Pseudo Labeling (GTPL) and Pseudo Label Dropout (PLD), which can be used separately or in combination. GTPL changes the threshold value of each category by combining the confidence of labeled and unlabeled samples. PLD alleviates the category imbalance by randomly discarding some of the pseudo labels. We apply GTPL and PLD to FixMatch and CoMatch and effectively improve their semi-supervised classification performance. We validate the effectiveness of our approach in skin lesion diagnosis on two long-tailed distributions of public medical images on the ISIC 2018 and ISIC 2019 challenge datasets, obtaining AUCs of 89.19%, 92.71%, 94.71%, and 94.76%, respectively, on four scales of labeled data from ISIC 2018.