DETDet: Dual Ensemble Teeth Detection
Kyoungyeon Choi Jaewon Shin Eunyi Lyou
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