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@article{zhang2021hyperbolic,
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@article{wang2023vison,
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@article{ayubcha2024improved,
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  author={Qiao, Zhi and Han, Linbin and Zhen, Xiantong and Gao, Jiahong and Qian, Zhen},
  booktitle={The International Conference on Computational Linguistics},
  year={2025}
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  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2025}
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  year={2024}
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  booktitle={European Conference on Computer Vision},
  year={2024}
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@inproceedings{jegou2017one,
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  booktitle={International Conference on Medical Imaging with Deep Learning},
  year={2019}
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@article{valanarasu2021kiu,
  title={Kiu-net: Overcomplete convolutional architectures for biomedical image and volumetric segmentation},
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  journal={IEEE Transactions on Medical Imaging},
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  author={Zhou, Zongwei and Sodha, Vatsal and Rahman Siddiquee, Md Mahfuzur and Feng, Ruibin and Tajbakhsh, Nima and Gotway, Michael B and Liang, Jianming},
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  year={2019}
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@article{wang2024susceptibility,
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  year={2021}
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  year={2017}
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@article{chen2020unsupervised,
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@article{gonzalez2025robust,
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@inproceedings{batson2019noise2self,
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  booktitle={International conference on machine learning},
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  year={2019},
  organization={PMLR}
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@article{wang2021image,
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  title={Dataset of breast ultrasound images},
  author={Al-Dhabyani, Walid and Gomaa, Mohammed and Khaled, Hussien and Fahmy, Aly},
  journal={Data in Brief},
  volume={28},
  pages={104863},
  year={2020},
  publisher={Elsevier}
}

@inproceedings{jha2019kvasir,
  title={Kvasir-seg: A segmented polyp dataset},
  author={Jha, Debesh and Smedsrud, Pia H and Riegler, Michael A and Halvorsen, P{\aa}l and De Lange, Thomas and Johansen, Dag and Johansen, H{\aa}vard D},
  booktitle={International Conference on Multimedia Modeling},
  year={2019}
}

@inproceedings{wei2021shallow,
  title={Shallow attention network for polyp segmentation},
  author={Wei, Jun and Hu, Yiwen and Zhang, Ruimao and Li, Zhen and Zhou, S Kevin and Cui, Shuguang},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2021}
}

@ARTICLE{Gonzalez2025rt,
  title     = "Accuracy of deep learning-based {AI} models for early caries
               lesion detection: the influence of annotation quality and
               reference choice",
  author    = "Gonzalez-Valenzuela, Ricardo E and Mettes, Pascal and Loos,
               Bruno G and Marquering, Henk and Berkhout, Erwin",
  abstract  = "Abstract Objectives The objective of this study is to assess how
               different annotation methods used during AI model training
               affect the accuracy of early caries lesion detection, and how
               the choice of the evaluation reference standard leads to
               significant differences in assessing AI models' outcomes.
               Clinical Relevance. AI-based tools for caries detection are
               becoming common in dentistry. This study shows that how these
               models are evaluated can significantly impact perceived
               performance. Clinicians and developers should ensure that
               evaluation standards are independent and clinically relevant to
               avoid overestimating AI's diagnostic abilities and to build
               trust for real-world use and regulatory approval. Methods
               Multiple AI caries lesion segmentation models were trained on
               the ACTA-DIRECT dataset using annotations from (1) single
               dentists, (2) aggregated strategies (majority vote, consensus
               meetings, STAPLE), and (3) micro-CT-based methods. Model
               accuracy was evaluated using two approaches: (1) comparison
               against micro-CT-based annotations and (2) comparison against
               the training-matched annotations. Statistical significance of
               differences in model diagnostic accuracy across annotation
               strategies was assessed using the McNemar test. Results There
               was no statistically significant difference in diagnostic
               accuracy among AI models when compared to micro-CT-based
               annotations. However, the diagnostic accuracy was considered
               statistically significantly higher when the results of the AI
               models were evaluated with the training-matched annotations.
               Conclusion Our findings indicate a strong influence of reference
               standards on AI model evaluation. While annotation strategies
               during training did not significantly affect AI accuracy in
               caries lesion segmentation, evaluation was subject to bias when
               models were tested against different reference standards.
               Clinical relevance AI-based tools for caries detection are
               becoming common in dentistry. This study shows that how these
               models are evaluated can significantly impact perceived
               performance. Clinicians and developers should ensure that
               evaluation standards are independent and clinically relevant to
               avoid overestimating AI's diagnostic abilities and to build
               trust for real-world use and regulatory approval. Graphical
               abstract",
  journal   = "Clinical Oral Investigations",
  publisher = "Springer Science and Business Media LLC",
  volume    =  29,
  number    =  12,
  month     =  dec,
  year      =  2025,
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}

@misc{tichy2023dental,
  title={Dental caries in bitewing radiographs. Mendeley Data},
  author={Tich{\`y}, A and Kunt, L and Kybic, J},
  year={2023}
}

@book{lee2018introduction,
  title={Introduction to Riemannian manifolds},
  author={Lee, John M},
  volume={2},
  year={2018},
  publisher={Springer}
}
