# introduction 
@article{chatzigianni2009geometric,
  title={Geometric morphometric evaluation of cervical vertebrae shape and its relationship to skeletal maturation},
  author={Chatzigianni, Athina and Halazonetis, Demetrios J},
  journal={American Journal of Orthodontics and Dentofacial Orthopedics},
  volume={136},
  number={4},
  pages={481--e1},
  year={2009},
  publisher={Elsevier}
}

@article{gu2007mandibular,
  title={Mandibular growth changes and cervical vertebral maturation: a cephalometric implant study},
  author={Gu, Yan and McNamara Jr, James A},
  journal={The Angle Orthodontist},
  volume={77},
  number={6},
  pages={947--953},
  year={2007}
}

@article{malta2009quantification,
  title={Quantification of cranial base growth during pubertal growth},
  author={Malta, Luciana Abrao and Ortolani, Cristina F and Faltin, Kurt},
  journal={Journal of orthodontics},
  volume={36},
  number={4},
  pages={229--235},
  year={2009},
  publisher={Taylor \& Francis}
}

@article{tao2022tooth,
  title={Tooth CT Image Segmentation Method Based on the U-Net Network and Attention Module},
  author={Tao, Sha and Wang, Zhenfeng},
  journal={Computational and Mathematical Methods in Medicine},
  volume={2022},
  number={1},
  pages={3289663},
  year={2022},
  publisher={Wiley Online Library}
}

# Related work citations
% CVM and ML
@article{amasya2020cervical,
  title={Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models},
  author={Amasya, Hakan and others},
  journal={Dentomaxillofacial Radiology},
  volume={49},
  number={5},
  pages={20190441},
  year={2020},
  publisher={Oxford University Press}
}

@article{kok2019usage,
  title={Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics},
  author={K{\"o}k, Hatice and others},
  journal={Progress in Orthodontics},
  volume={20},
  pages={1--10},
  year={2019},
  publisher={Springer}
}

@article{amasya2020validation,
  title={Validation of cervical vertebral maturation stages: Artificial intelligence vs human observer visual analysis},
  author={Amasya, Hakan and others},
  journal={American Journal of Orthodontics and Dentofacial Orthopedics},
  volume={158},
  number={6},
  pages={e173--e179},
  year={2020},
  publisher={Elsevier}
}


% Succes DL medical imaging
@inproceedings{schneider2023one,
  title={Is one label all you need? Single positive multi-label training in medical image analysis},
  author={Schneider, Helen and others},
  booktitle={2023 IEEE International Conference on Big Data (BigData)},
  pages={4963--4972},
  year={2023},
  organization={IEEE}
}

@article{schneider2024informed,
  title={Informed Deep Abstaining Classifier: Investigating noise-robust training for diagnostic decision support systems},
  author={Schneider, Helen and others},
  journal={arXiv preprint arXiv:2410.21014},
  year={2024}
}

@inproceedings{schneider2023symmetry,
  title={Symmetry-aware siamese network: Exploiting pathological asymmetry for chest x-ray analysis},
  author={Schneider, Helen and others},
  booktitle={International Conference on Artificial Neural Networks},
  pages={165--176},
  year={2023},
  organization={Springer}
}

@inproceedings{schneider2023segmentation,
  title={Segmentation and analysis of lumbar spine mri scans for vertebral body measurements},
  author={Schneider, Helen and others},
  booktitle={The European Symposium on Artificial Neural Networks},
  year={2023}
}

% DL and Dentristy
@article{hwang2019overview,
  title={An overview of deep learning in the field of dentistry},
  author={Hwang, Jae-Joon and others},
  journal={Imaging science in dentistry},
  volume={49},
  number={1},
  pages={1--7},
  year={2019},
  publisher={Korean Academy of Oral and Maxillofacial Radiology}
}

@article{lin2022detecting,
  title={Detecting proximal caries on periapical radiographs using convolutional neural networks with different training strategies on small datasets},
  author={Lin, Xiujiao and others},
  journal={Diagnostics},
  volume={12},
  number={5},
  pages={1047},
  year={2022},
  publisher={MDPI}
}

% CVM and DL
@article{liao2022icvm,
  title={ICVM: An interpretable deep learning model for CVM assessment under label uncertainty},
  author={Liao, Ni and others},
  journal={IEEE Journal of Biomedical and Health Informatics},
  volume={26},
  number={8},
  pages={4325--4334},
  year={2022},
  publisher={IEEE}
}
@article{mohammad2022deep,
  title={Deep learning for the classification of cervical maturation degree and pubertal growth spurts: A pilot study},
  author={Mohammad-Rahimi, Hossein and others},
  journal={Korean journal of orthodontics},
  volume={52},
  number={2},
  pages={112--122},
  year={2022},
  publisher={Korean Association of Orthodontists}
}
@article{zhou2021development,
  title={Development of an artificial intelligence system for the automatic evaluation of cervical vertebral maturation status},
  author={Zhou, Jing and others},
  journal={Diagnostics},
  volume={11},
  number={12},
  pages={2200},
  year={2021},
  publisher={MDPI}
}
@article{seo2021comparison,
  title={Comparison of deep learning models for cervical vertebral maturation stage classification on lateral cephalometric radiographs},
  author={Seo, Hyejun and others},
  journal={Journal of Clinical Medicine},
  volume={10},
  number={16},
  pages={3591},
  year={2021},
  publisher={MDPI}
}

@article{makaremi2019deep,
  title={Deep learning and artificial intelligence for the determination of the cervical vertebra maturation degree from lateral radiography},
  author={Makaremi, Masrour and Lacaule, Camille and Mohammad-Djafari, Ali},
  journal={Entropy},
  volume={21},
  number={12},
  pages={1222},
  year={2019},
  publisher={MDPI}
}

@article{kim2021estimating,
  title={Estimating cervical vertebral maturation with a lateral cephalogram using the convolutional neural network},
  author={Kim, Eun-Gyeong and others},
  journal={Journal of Clinical Medicine},
  volume={10},
  number={22},
  pages={5400},
  year={2021},
  publisher={MDPI}
}

% FSL 
@article{wang2020generalizing,
  title={Generalizing from a few examples: A survey on few-shot learning},
  author={Wang, Yaqing and others},
  journal={ACM computing surveys (csur)},
  volume={53},
  number={3},
  pages={1--34},
  year={2020},
  publisher={ACM New York, NY, USA}
}

@article{parnami2022learning,
  title={Learning from few examples: A summary of approaches to few-shot learning},
  author={Parnami, Archit and others},
  journal={arXiv preprint arXiv:2203.04291},
  year={2022}
}
 %FSL and dentristy
@article{kim2024unsupervised,
  title={Unsupervised few shot learning architecture for diagnosis of periodontal disease in dental panoramic radiographs},
  author={Kim, Min Joo and Chae, Sun Geu and Bae, Suk Joo and Hwang, Kyung-Gyun},
  journal={Scientific Reports},
  volume={14},
  number={1},
  pages={23237},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

@article{yang2023medmnist,
  title={Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification},
  author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and others},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={41},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

 %Few-shot learning 
@article{snell2017prototypical,
  title={Prototypical networks for few-shot learning},
  author={Snell, Jake and Swersky, Kevin and Zemel, Richard},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{khosla2020supervised,
  title={Supervised contrastive learning},
  author={Khosla, Prannay and Teterwak, Piotr and Wang, Chen and Sarna, Aaron and Tian, Yonglong and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={18661--18673},
  year={2020}
}

% Grad-CAM

@misc{jacobgilpytorchcam,
  title={PyTorch library for CAM methods},
  author={Jacob Gildenblat and contributors},
  year={2021},
  publisher={GitHub},
}

# CVM methods

@article{Lamparski1975SkeletalAA,
  title={Skeletal age assessment utilizing cervical vertebrae},
  author={Don G. Lamparski},
  journal={American Journal of Orthodontics},
  year={1975},
  volume={67},
  pages={458-459},
}

@article{BACCETTI2005119,
title = {The Cervical Vertebral Maturation (CVM) Method for the Assessment of Optimal Treatment Timing in Dentofacial Orthopedics},
journal = {Seminars in Orthodontics},
volume = {11},
number = {3},
pages = {119-129},
year = {2005},
note = {Early Timely Orthodontic Treatment},
issn = {1073-8746},
doi = {https://doi.org/10.1053/j.sodo.2005.04.005},
author = {Tiziano Baccetti and Lorenzo Franchi and James A. McNamara},
}

@article{10.2319/111517-787.1,
    author = {McNamara, James A., Jr and Franchi, Lorenzo},
    title = {The cervical vertebral maturation method: A user's guide},
    journal = {The Angle Orthodontist},
    volume = {88},
    number = {2},
    pages = {133-143},
    year = {2018},
    month = {01},
    issn = {0003-3219},
    doi = {10.2319/111517-787.1},
}

# SAM-med2d

@misc{cheng2023sammed2d,
      title={SAM-Med2D}, 
      author={Junlong Cheng and Jin Ye and Zhongying Deng and Jianpin Chen and Tianbin Li and others},
      year={2023},
      eprint={2308.16184},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

# SAM

@article{kirillov2023segany,
  title={Segment Anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and others},
  journal={arXiv:2304.02643},
  year={2023}
}
