Self-Supervised Learning with Masked Image Modeling for Teeth Numbering, Detection of Dental Restorations, and Instance Segmentation in Dental Panoramic Radiographs. (arXiv:2210.11404v1 [cs.CV])

The computer-assisted radiologic informative report is currently emerging in
dental practice to facilitate dental care and reduce time consumption in manual
panoramic radiographic interpretation. However, the amount of dental
radiographs for training is very limited, particularly from the point of view
of deep learning. This study aims to utilize recent self-supervised learning
methods like SimMIM and UM-MAE to increase the model efficiency and
understanding of the limited number of dental radiographs. We use the Swin
Transformer for teeth numbering, detection …

arxiv dental detection image modeling segmentation self-supervised learning supervised learning