Description
Book SynopsisConvolutional Neural Network-based Multi-scale Semantic Segmentation for Two-dimensional Panoramic X-rays of Teeth.- TB-FPN: Enhancing Tooth Segmentation with Cascade Boundary-aware FPN.- Perform Special Post-processing after Tooth Segmentation.- A Multi-Stage Framework for 3D Individual Tooth Segmentation in Dental CBCT.- Preprocessing of Prior Knowledge before Semi-Supervised Tooth Segmentation.- A Semi-Supervised Tooth Segmentation Method based on Entropy-Guided Mean Teacher and Weakly Mutual Consistency Network.- MsNet: Multi-Stage Learning from Seldom Labeled Data for 3D Tooth Segmentation in Dental Cone Beam Computed Tomography.- Diffusion-Based Conv-Former Dual-Encode U-Net: DDPM for Level Set Evolution Mapping - MICCAI STS 2023 Challenge.- Semi-Supervised 3D Tooth Segmentation Using nn-UNet with Axial Attention and Positional Correction.- Boundary Feature Fusion Network for Tooth Image Segmentation.- Self-training Based Semi-Supervised Learning and U-Net with Denoiser for Teeth Segmentation in X-ray Image.- UX-CNet: Effective Edge Information Acquisition for Teeth Image Segmentation.- 2D Teeth Segmentation Base on Half-image Approach and VCMix-Net+.- Automated Dental CBCT Segmentation using Pseudo Labeling Method.- Prior-aware Cross Pseudo Supervision for Semi-supervised Tooth Segmentation.- High-Precision Semi-supervised 3D Dental Segmentation Based on nnUNet.
