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.

Recent research on semi-supervised learning (SSL) is mainly based on the method of consistency regularization, which relies on data augmentation in the specific domain. Pseudolabelling is a more general method that has no such restrictions but is limited by noisy training. Medical datasets are a particular domain that exhibit a long-tail distribution. Combining these two limitations, we focus on the widespread use of weak augmentation to generate pseudolabels. We propose FixMatch-LS and a variant FixMatch-LS-v2 for medical image classification. First, we introduce label smoothing to change the pseudolabel threshold, which reduces the influence of noisy pseudolabels. In addition, pseudolabelling should be matched with consistency. A suitable consistency can constrain pseudolabelling to improve the quality of the pseudolabels. We validate our framework on skin lesion diagnoses from the ISIC 2018 and ISIC 2019 challenges, obtaining AUCs of 91.63%, 93.70%, 94.46%, and 95.44% on the four proportions of labelled data from ISIC 2018.

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.

BACKGROUND:Semi-supervised learning (SSL) can effectively use information from unlabeled data to improve model performance, which has great significance in medical imaging tasks. Pseudo-labeling is a classical SSL method that uses a model to predict unlabeled samples and selects the prediction with the highest confidence level as the pseudo-labels and then uses the generated pseudo-labels to train the model. Most of the current pseudo-label-based SSL algorithms use predefined fixed thresholds for all classes to select unlabeled data.PURPOSE:However, data imbalance is a common problem in medical image tasks, where the use of fixed threshold to generate pseudo-labels ignores different classes of learning status and learning difficulties. The aim of this study is to develop an algorithm to solve this problem.METHODS:In this work, we propose Multi-Curriculum Pseudo-Labeling (MCPL), which evaluates the learning status of the model for each class at each epoch and automatically adjusts the thresholds for each class. We apply MCPL to FixMatch and propose a new SSL framework for medical image classification, which we call the improved algorithm FaxMatch. To mitigate the impact of incorrect pseudo-labels on the model, we use label smoothing (LS) strategy to generate soft labels (SL) for pseudo-labels.RESULTS:We have conducted extensive experiments to evaluate our method on two public benchmark medical image classification datasets: the ISIC 2018 skin lesion analysis and COVID-CT datasets. Experimental results show that our method outperforms fully supervised baseline, which uses only labeled data to train the model. Moreover, our method also outperforms other state-of-the-art methods.CONCLUSIONS:We propose MCPL and construct a semi-supervised medical image classification framework to reduce the reliance of the model on a large number of labeled images and reduce the manual workload of labeling medical image data.