Panoramic X-ray is a simple and effective tool for diagnosing dental diseases
in clinical practice. When deep learning models are developed to assist dentist
in interpreting panoramic X-rays, most of their performance suffers from the
limited annotated data, which requires dentist's expertise and a lot of time
cost. Although self-supervised learning (SSL) has been proposed to address this
challenge, the two-stage process of pretraining and fine-tuning requires even
more training time and computational resources. In this paper, we present a
self-supervised auxiliary detection (SSAD) framework, which is plug-and-play
and compatible with any detectors. It consists of a reconstruction branch and a
detection branch. Both branches are trained simultaneously, sharing the same
encoder, without the need for finetuning. The reconstruction branch learns to
restore the tooth texture of healthy or diseased teeth, while the detection
branch utilizes these learned features for diagnosis. To enhance the encoder's
ability to capture fine-grained features, we incorporate the image encoder of
SAM to construct a texture consistency (TC) loss, which extracts image
embedding from the input and output of reconstruction branch, and then enforces
both embedding into the same feature space. Extensive experiments on the public
DENTEX dataset through three detection tasks demonstrate that the proposed SSAD
framework achieves state-of-the-art performance compared to mainstream object
detection methods and SSL methods. The code is available at
this https URL

本文提出了一种自监督辅助检测 (SSAD) 框架，用于诊断牙科疾病，通过共享相同的编码器和无需微调的方式，同时训练重建分支和检测分支，以提高对细粒度特征的捕捉能力。实验证明，该框架在公共 DENTEX 数据集上的三项检测任务中取得了与主流目标检测方法和自监督学习方法相比的最先进性能。