Abstract
Addressing the growing need for advanced computer-aided diagnostic tools in dentistry, this study presents DPML (Dental Prior-guided Multitask Learning), a pioneering framework tailored for the analysis of dental panoramic radiographs in the face of data scarcity. DPML introduces a novel multitask learning strategy that effectively mitigates the limitations of sparse, well-annotated dental datasets. The methodology integrates four advanced techniques: targeted data augmentation for enhanced image diversity, segmentation structures for improved edge definition, polar-coordinate methods for precise tooth contour extraction, and a spatially informed classification strategy for accurate tooth type identification. Our comparative analysis demonstrates that DPML markedly surpasses traditional approaches in tooth segmentation, detection, and classification. Remarkably, it achieves a 95.06% mean Average Precision (mAP) and a 97.70% precision rate in dental object recognition tasks on a small-size dataset.
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