DETDet: Dual Ensemble Teeth Detection
This work addresses the need for improved diagnostic accuracy in digital dentistry for dental practitioners, but it appears incremental as it builds on existing methods like Mask-RCNN, DiffusionDet, and DINO for a specific challenge.
The paper tackles the problem of detecting and diagnosing teeth in dental panoramic X-rays by introducing DETDet, a dual ensemble network, achieving enhanced performance as part of the 2023 MICCAI DENTEX challenge, though no concrete numbers are provided in the 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