DETDet: Dual Ensemble Teeth Detection
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Abstract (arXiv)
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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References · 7
- 2017Mask R-CNNcited by 7,266
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,426
- 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,312
- 2019Hybrid Task Cascade for Instance Segmentationcited by 479
- 2019Cascade R-CNN: High Quality Object Detection and Instance Segmentationcited by 428
- 2022DiffusionDet: Diffusion Model for Object Detectioncited by 354
- 2023Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rayscited by 18
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