Tooth Instance Segmentation and Disease Detection With Uncertainty-Aware Contrastive Learning and Cross-Scale Attention

Recent years have witnessed the increasing applications of artificial intelligence for tooth treatment, among which tooth instance segmentation and disease detection are two important research directions. Advanced algorithms have been proposed, however, two challenging issues remain unsolved, i.e., unclear prediction boundaries for adjacent teeth, and high parameters of the model. To this end, our work proposes a lightweight framework, namely UCL-Net, for efficient tooth instance segmentation and disease detection. Specifically, uncertainty-aware contrastive learning is first employed for tooth segmentation. It is based on a multivariate Gaussian distribution to model the boundary pixel and is able to highlight inter-class differences, thereby refining the segmentation boundary. In addition, a lightweight segmentation model which has only 34.9 M parameters is further developed. Benefiting from the cross-scale attention, it is able to efficiently fuse different scale features, and therefore yields accurate tooth disease detection with a lightweight load. Four benchmark datasets are employed for performance validation. Both the qualitative and quantitative results demonstrate that the proposed UCL-Net is lightweight, effective, and advantageous over peer state-of-the-art (SOTA) methods.