  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 of dental restorations, and instance
segmentation tasks. To the best of our knowledge, this is the first study that
applied self-supervised learning methods to Swin Transformer on dental
panoramic radiographs. Our results show that the SimMIM method obtained the
highest performance of 90.4% and 88.9% on detecting teeth and dental
restorations and instance segmentation, respectively, increasing the average
precision by 13.4 and 12.8 over the random initialization baseline. Moreover,
we augment and correct the existing dataset of panoramic radiographs. The code
and the dataset are available at https://github.com/AmaniHAlmalki/DentalMIM.
