论文标题
牙齿编号，牙科修复体检测和实例分割的牙齿射线照相仪的掩盖图像建模的自我监督学习
Self-Supervised Learning with Masked Image Modeling for Teeth Numbering, Detection of Dental Restorations, and Instance Segmentation in Dental Panoramic Radiographs
论文作者
论文摘要
目前，牙科实践中正在出现计算机辅助的放射学信息报告，以促进牙齿护理并降低手动全景放射线解释中的时间消耗。但是，用于训练的牙齿X光片的数量非常有限，尤其是从深度学习的角度来看。这项研究旨在利用诸如Simmim和UM-MAE等最新的自我监督学习方法来提高模型效率并了解有限的牙齿X光片。我们使用SWIN Transformer进行牙齿编号，牙科修复体的检测以及实例分割任务。据我们所知，这是第一个将自我监督的学习方法应用于牙科全景射线照片上的研究。我们的结果表明，Simmim方法在检测牙齿和牙齿修复体和实例分割方面获得了90.4％和88.9％的最高性能，而在随机初始化基线上，平均精度提高了13.4和12.8。此外，我们增加并纠正了全景X光片的现有数据集。代码和数据集可在https://github.com/amanihalmalki/dentalmim上找到。
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.
