Acknowledgement
This work was conducted with support from the 2023 Chungwoon University Academic Research Fund.
References
- T. He, et al., "Bag of Tricks for Image Classification with Convolutional Neural Networks," in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019 pp. 558-567. DOI: https://doi.org/10.48550/arXiv.1812.01187
- J. J. Park, et al., "Teeth Segmentation for Orthodontics based on Deep Learning," The Transactions of the Korean Institute of Electrical Engineers, vol. 72, no. 3, pp. 440~446, 2023. DOI: https://doi.org/10.5370/KIEE.2023.72.3.440
- J. J. Park, et al., "Semantic Segmentation of Teeth using Layered UNet," The Transactions of the Korean Institute of Electrical Engineers, vol. 72, no. 11, pp. 1470~1476, 2023. DOI: https://doi.org/10.5370/KIEE.2023.72.11.1470
- https://keymakr.com/blog/semantic-segmentation-uses-and-applications/
- https://en.wikipedia.org/wiki/FDI_World_Dental_Federation_notation
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
- P. Goyal, P. Dollar, R. B. Girshick, P. Noordhuis, ' L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He, "Accurate, large minibatch SGD: training imagenet in 1 hour," CoRR, abs/1706.02677, 2017.
- Yo-wei Chen, et al., "Artificial intelligence in dentistry: current applications and future perspectives," QUINTESSENCE INTERNATIONAL, vol. 51, no. 3, March 2020. DOI: https://doi.org/10.3290/j.qi.a43952
- H. Ding et al., "Artificial intelligence in dentistry-A review," vol. 4, Front. Dent. Med. Sec. Dental Materials, 20 February 2023 DOI: https://doi.org/10.3389/fdmed.2023.1085251
- Khanagar SB, Al-ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, et al, "Developments, application, and performance of artificial intelligence in dentistry-a systematic review," J Dent Sci. 16(1):508-22, 2021. DOI: https://doi.org/10.1016/j.jds.2020.06.019