DETDet: Dual Ensemble Teeth Detection

Kyoungyeon Choi, Jaewon Shin, Eunyi Lyou
8/27/2023
cs.CV

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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

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Cite this paper

@article{choi2023detdet,
  title  = {DETDet: Dual Ensemble Teeth Detection},
  author = {Kyoungyeon Choi and Jaewon Shin and Eunyi Lyou},
  year   = {2023},
  eprint = {2308.14070},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.14070v1}
}

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