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Multi-Encoding Contrastive Learning for Dual-Stream Self-Supervised 3D Dental Segmentation Network
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DOI：10.3390/technologies13090419.png)
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Abstract
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To address the limitation regarding the supervised dataset scale in the semantic recognition of newly distributed types such as wisdom teeth and missing teeth, the multi-encoding contrastive learning for dual-stream self-supervised 3D dental segmentation network (MECSegNet) is proposed. First, a self-supervised encoder pre-training framework is designed by integrating 3D mesh feature representation to construct a deep feature encoding network, where the pre-trained encoder learns universal dental feature representations. Then, a multi-contrastive loss function is established to jointly optimize the self-supervised encoder, extracting effective local and global feature representations while incorporating a cross-stream contrastive loss to learn discriminative features from multiple perspectives. Finally, the improved encoder is integrated into a dual-stream network to build a fine-tuning framework for supervised fine-tuning on a small proportion of labeled data. Experimental results show that, with only 20% labeled data, the proposed MECSegNet achieves a 1.3% improvement in accuracy and a 79.81% reduction in computational cost compared to existing self-supervised methods, while maintaining comparable segmentation accuracy and efficiency to high-performance fully supervised methods.
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