Class-Specific Thresholding for Imbalanced Semi-Supervised Learning

Semi-supervised learning (SSL) has emerged as a powerful technique to mitigate the scarcity of labeled data. However, the effectiveness of most SSL methods relies on the assumption of a balanced class distribution, which often proves unrealistic, especially in medical imaging scenarios. To address this challenge, we propose Class-Specific Thresholding (CST) for imbalanced SSL. Specifically, CST dynamically integrates the model's learning status, class-specific learning effects, and data class distribution to estimate confidence thresholds for each class. These thresholds enhance the reliability of selected unlabeled data, particularly for minority classes. Additionally, we introduce a class-sensitive unsupervised loss function that further intensifies the model's focus on minority classes at minimal computational cost by leveraging predicted class distributions from previous inputs. Extensive experiments on highly imbalanced skin disease and endoscopy image datasets demonstrate that CST significantly outperforms state-of-the-art SSL methods, particularly in improving the accuracy for minority classes. These results validate the effectiveness of CST in addressing class imbalance.