Multi-Category Fusion Contrastive Learning with Core Data Selection for Robust RGB Image-based Dental Caries Classification

Dental caries represents one of the most prevalent diseases affecting humankind, particularly among adolescent populations. RGB images offer a convenient and cost-effective method for dental caries detection. However, the image data captured may suffer from blurriness, which, together with label errors introduced during manual annotations, can degrade the performance of the model learned for dental caries detection. To address this problem, we propose the Multi-Category Fusion Contrastive Learning with Core Data Selection (M3C) to improve the predictive performance of dental caries classification models. Instead of fine-tuning the backbone network structure, M3C focuses on improving the robustness of model to label errors from a novel perspective by identifying core data that is highly relevant to the dental caries category. We analyzed and validated that M3C has better robustness in dental caries detection from model architecture representation, theoretical analysis, and mutual information computation. Specifically, M3C quantifies the average mutual information between dental caries images and dental caries category centers based on Jensen-Shannon Divergence (JSD), which is then used for selecting the core data to mitigate the impact of label errors on model performance. Furthermore, we design inter-category contrastive learning to enhance the performance of the model in distinguishing the categories of dental caries by improving the feature representation for samples of different categories. With theoretical justification, we jointly optimized model training using prediction loss and confusion contrastive loss. Extensive experiments demonstrate that M3C significantly surpasses comparative data selection methods in dental caries detection on dental caries RGB image datasets. More excitingly, M3C achieves superior predictive performance using only 50% of the core data compared to state-of-the-art dental caries detection methods using the entire dataset. Our code is available at: https://github.com/papercodeforreview/Caries_Detection_Journal.