Enhanced Masked Image Modeling for Analysis of Dental Panoramic Radiographs
It addresses the challenge of limited annotated dental radiographs for computer-assisted diagnosis, which is incremental as it builds on existing self-supervised learning techniques.
This study tackled the problem of limited training data for dental image analysis by proposing SD-SimMIM, a self-distillation enhanced self-supervised learning method based on masked image modeling, which outperformed other methods on tasks like teeth numbering and detection.
The computer-assisted radiologic informative report has received increasing research attention to facilitate diagnosis and treatment planning for dental care providers. However, manual interpretation of dental images is limited, expensive, and time-consuming. Another barrier in dental imaging is the limited number of available images for training, which is a challenge in the era of deep learning. This study proposes a novel self-distillation (SD) enhanced self-supervised learning on top of the masked image modeling (SimMIM) Transformer, called SD-SimMIM, to improve the outcome with a limited number of dental radiographs. In addition to the prediction loss on masked patches, SD-SimMIM computes the self-distillation loss on the visible patches. We apply SD-SimMIM on dental panoramic X-rays for teeth numbering, detection of dental restorations and orthodontic appliances, and instance segmentation tasks. Our results show that SD-SimMIM outperforms other self-supervised learning methods. Furthermore, we augment and improve the annotation of an existing dataset of panoramic X-rays.