SSAD: Self-supervised Auxiliary Detection Framework for Panoramic X-ray based Dental Disease Diagnosis

TL;DR

The paper introduces SSAD, a plug-and-play self-supervised framework for dental disease detection in panoramic X-rays that improves performance without additional fine-tuning, leveraging shared encoder and texture consistency loss.

Contribution

The novel SSAD framework combines reconstruction and detection branches with shared encoder and texture consistency loss, enabling effective self-supervised learning for dental disease detection.

Findings

Achieves state-of-the-art results on DENTEX dataset

Outperforms mainstream detection and SSL methods

No fine-tuning required after training

Abstract

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 https://github.com/Dylonsword/SSAD