Figure 3
From: Dental caries detection using a semi-supervised learning approach
An overview of our proposed self-supervised learning-based method for dental caries segmentation. Firstly, the training data is re-sampled through our centroid cropping-based sampling (CCS) approach that initially extracts the cavity region from the input images and employs state-of-the-art transformation techniques to increase the data samples. Secondly, the teacher model \({\mathscr {M}}_T\) is trained in a fully supervised learning fashion on real data (to guarantee high-quality pseudo-label generation), which is then used to generate pseudo labels for unlabelled images for training student model \({\mathscr {M}}_S\). Lastly, the student model is trained on both the real and pseudo labels to ensure better generalization.
