DETDet: Dual Ensemble Teeth Detection

TL;DR

DETDet is a novel dual-module AI system designed for dental X-ray analysis, combining ensemble and mask-based techniques to improve teeth detection and diagnosis accuracy, advancing digital dentistry diagnostics.

Contribution

The paper introduces DETDet, a dual ensemble network utilizing Mask-RCNN and combined diffusion and DINO models, with unlabeled data integration for enhanced dental X-ray analysis.

Findings

Improved detection accuracy over existing methods

Effective use of unlabeled data enhances performance

Dual-module design separates enumeration and diagnosis tasks

Abstract

The field of dentistry is in the era of digital transformation. Particularly, artificial intelligence is anticipated to play a significant role in digital dentistry. AI holds the potential to significantly assist dental practitioners and elevate diagnostic accuracy. In alignment with this vision, the 2023 MICCAI DENTEX challenge aims to enhance the performance of dental panoramic X-ray diagnosis and enumeration through technological advancement. In response, we introduce DETDet, a Dual Ensemble Teeth Detection network. DETDet encompasses two distinct modules dedicated to enumeration and diagnosis. Leveraging the advantages of teeth mask data, we employ Mask-RCNN for the enumeration module. For the diagnosis module, we adopt an ensemble model comprising DiffusionDet and DINO. To further enhance precision scores, we integrate a complementary module to harness the potential of unlabeled data. The code for our approach will be made accessible at https://github.com/Bestever-choi/Evident