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The computer-assisted radiologic informative report is currently emerging indental practice to facilitate dental care and reduce time consumption in manualpanoramic radiographic interpretation. However, the amount of dentalradiographs for training is very limited, particularly from the point of viewof deep learning. This study aims to utilize recent self-supervised learningmethods like SimMIM and UM-MAE to increase the model efficiency andunderstanding of the limited number of dental radiographs. We use the SwinTransformer for teeth numbering, detection of dental restorations, and instancesegmentation tasks. To the best of our knowledge, this is the first study thatapplied self-supervised learning methods to Swin Transformer on dentalpanoramic radiographs. Our results show that the SimMIM method obtained thehighest performance of 90.4% and 88.9% on detecting teeth and dentalrestorations and instance segmentation, respectively, increasing the averageprecision by 13.4 and 12.8 over the random initialization baseline. Moreover,we augment and correct the existing dataset of panoramic radiographs. The codeand the dataset are available at https://github.com/AmaniHAlmalki/DentalMIM.

Self-Supervised Learning with Masked Image Modeling for Teeth Numbering,  Detection of Dental Restorations, and Instance Segmentation in Dental  Panoramic Radiographs