@article{chorev2022deepchecks,
  title={Deepchecks: A library for testing and validating machine learning models and data},
  author={Chorev, Shir and Tannor, Philip and Israel, Dan Ben and Bressler, Noam and Gabbay, Itay and Hutnik, Nir and Liberman, Jonatan and Perlmutter, Matan and Romanyshyn, Yurii and Rokach, Lior},
  journal={Journal of Machine Learning Research},
  volume={23},
  number={285},
  pages={1--6},
  year={2022}}

@article{huang2020survey,
  title={A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability},
  author={Huang, Xiaowei and Kroening, Daniel and Ruan, Wenjie and Sharp, James and Sun, Youcheng and Thamo, Emese and Wu, Min and Yi, Xinping},
  journal={Computer Science Review},
  volume={37},
  pages={100270},
  year={2020},
  publisher={Elsevier}
}

@article{kim2020inconsistency,
  title={Inconsistency in the use of the term “validation” in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging},
  author={Kim, Dong Wook and Jang, Hye Young and Ko, Yousun and Son, Jung Hee and Kim, Pyeong Hwa and Kim, Seon-Ok and Lim, Joon Seo and Park, Seong Ho},
  journal={Plos one},
  volume={15},
  number={9},
  pages={e0238908},
  year={2020},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{frounchi2011automating,
  title={Automating image segmentation verification and validation by learning test oracles},
  author={Frounchi, Kambiz and Briand, Lionel C and Grady, Leo and Labiche, Yvan and Subramanyan, Rajesh},
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  year={2011},
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}


@article{badran2022validation,
  title={Validation of deep learning segmentation of CT images of fiber-reinforced composites},
  author={Badran, Aly and Parkinson, Dula and Ushizima, Daniela and Marshall, David and Maillet, Emmanuel},
  journal={Journal of Composites Science},
  volume={6},
  number={2},
  pages={60},
  year={2022},
  publisher={MDPI}
}

@article{kamann2021benchmarking,
  title={Benchmarking the robustness of semantic segmentation models with respect to common corruptions},
  author={Kamann, Christoph and Rother, Carsten},
  journal={International journal of computer vision},
  volume={129},
  number={2},
  pages={462--483},
  year={2021},
  publisher={Springer}
}

@inproceedings{arnab2018robustness,
  title={On the robustness of semantic segmentation models to adversarial attacks},
  author={Arnab, Anurag and Miksik, Ondrej and Torr, Philip HS},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={888--897},
  year={2018}
}

@inproceedings{schiappa2024robustness,
  title={Robustness analysis on foundational segmentation models},
  author={Schiappa, Madeline Chantry and Azad, Shehreen and Vs, Sachidanand and Ge, Yunhao and Miksik, Ondrej and Rawat, Yogesh S and Vineet, Vibhav},
  booktitle={2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  pages={1786--1796},
  year={2024},
  organization={IEEE}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% medical-related -segmentation
@article{rodriguez2022role,
  title={On the role of deep learning model complexity in adversarial robustness for medical images},
  author={Rodriguez, David and Nayak, Tapsya and Chen, Yidong and Krishnan, Ram and Huang, Yufei},
  journal={BMC Medical Informatics and Decision Making},
  volume={22},
  number={Suppl 2},
  pages={160},
  publisher={Springer},
  year={2022}
}

@article{yu2025evaluating,
  title={Evaluating the robustness of deep learning models trained to diagnose idiopathic pulmonary fibrosis using a retrospective study},
  author={Yu, Wenxi and McNitt-Gray, Michael F and Goldin, Jonathan G and Song, Jin Woo and Kim, Grace Hyun J},
  journal={Medical Physics},
  volume={52},
  number={6},
  pages={4239--4249},
  year={2025},
  publisher={Wiley Online Library}
}
@article{javed2024robustness,
  title={Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications},
  author={Javed, Haseeb and El-Sappagh, Shaker and Abuhmed, Tamer},
  journal={Artificial Intelligence Review},
  volume={58},
  number={1},
  pages={12},
  year={2024},
  publisher={Springer}
}
@inproceedings{paschali2018generalizability,
  title={Generalizability vs. robustness: investigating medical imaging networks using adversarial examples},
  author={Paschali, Magdalini and Conjeti, Sailesh and Navarro, Fernando and Navab, Nassir},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={493--501},
  year={2018},
  organization={Springer}
}

@article{liu2021robustifying,
  title={Robustifying deep networks for medical image segmentation},
  author={Liu, Zheng and Zhang, Jinnian and Jog, Varun and Loh, Po-Ling and McMillan, Alan B},
  journal={Journal of digital imaging},
  volume={34},
  number={5},
  pages={1279--1293},
  year={2021},
  publisher={Springer}
}

@article{galati2022accuracy,
  title={From accuracy to reliability and robustness in cardiac magnetic resonance image segmentation: a review},
  author={Galati, Francesco and Ourselin, S{\'e}bastien and Zuluaga, Maria A},
  journal={Applied Sciences},
  volume={12},
  number={8},
  pages={3936},
  year={2022},
  publisher={MDPI}
}

@article{tran2025strategies,
  title={Strategies to improve the robustness and generalizability of deep learning segmentation and classification in neuroimaging},
  author={Tran, Anh T and Zeevi, Tal and Payabvash, Seyedmehdi},
  journal={BioMedInformatics},
  volume={5},
  number={2},
  pages={20},
  year={2025},
  publisher={MDPI}
}
%%%%%%%%%%%%%%%%%%%%%
%%%% Bayesian

@article{lakshminarayanan2017simple,
  title={Simple and scalable predictive uncertainty estimation using deep ensembles},
  author={Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
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@article{carannante2024basis,
  title={BaSIS-Net: From point estimate to predictive distribution in neural networks-a Bayesian sequential importance sampling framework},
  author={Carannante, Giuseppina and Bouaynaya, Nidhal and Rasool, Ghulam and Mihaylova, Lyudmila},
  journal={Transactions on Machine Learning Research},
  year={2024}
}

@article{carannante2025super,
  title={SUPER-Net: Trustworthy image segmentation via uncertainty propagation in encoder-decoder networks},
  author={Carannante, Giuseppina and Bouaynaya, Nidhal C and Dera, Dimah and Fathallah-Shaykh, Hassan M and Rasool, Ghulam},
  journal={Pattern Recognition},
  pages={112503},
  year={2025},
  publisher={Elsevier}
}

@inproceedings{gal2016dropout,
  title={Dropout as a bayesian approximation: Representing model uncertainty in deep learning},
  author={Gal, Yarin and Ghahramani, Zoubin},
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  year={2016},
  organization={PMLR}
}

@inproceedings{blundell2015weight,
  title={Weight uncertainty in neural network},
  author={Blundell, Charles and Cornebise, Julien and Kavukcuoglu, Koray and Wierstra, Daan},
  booktitle={International conference on machine learning},
  pages={1613--1622},
  year={2015},
  organization={PMLR}
}

@article{dera2021premium,
  title={PremiUm-CNN: Propagating uncertainty towards robust convolutional neural networks},
  author={Dera, Dimah and Bouaynaya, Nidhal Carla and Rasool, Ghulam and Shterenberg, Roman and Fathallah-Shaykh, Hassan M},
  journal={IEEE Transactions on Signal Processing},
  volume={69},
  pages={4669--4684},
  year={2021},
  publisher={IEEE}
}

@book{ng2020estimating,
  title={Estimating uncertainty in neural networks for cardiac mri segmentation},
  author={Ng, Matthew},
  year={2020},
  publisher={University of Toronto (Canada)}
}

@article{scalco2024uncertainty,
  title={Uncertainty quantification in multi-class segmentation: Comparison between Bayesian and non-Bayesian approaches in a clinical perspective},
  author={Scalco, Elisa and Pozzi, Silvia and Rizzo, Giovanna and Lanzarone, Ettore},
  journal={Medical Physics},
  volume={51},
  number={9},
  pages={6090--6102},
  year={2024},
  publisher={Wiley Online Library}
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@article{sikha2025uncertainty,
  title={Uncertainty-aware segmentation quality prediction via deep learning Bayesian Modeling: Comprehensive evaluation and interpretation on skin cancer and liver segmentation},
  author={Sikha, OK and Riera-Marin, Meritxell and Galdran, Adrian and Lopez, Javier Garcia and Rodriguez-Comas, Julia and Piella, Gemma and Ballester, Miguel A Gonzalez},
  journal={Computerized Medical Imaging and Graphics},
  volume={123},
  pages={102547},
  year={2025},
  publisher={Elsevier}
}

@article{soufi2025validation,
  title={Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images},
  author={Soufi, Mazen and Otake, Yoshito and Iwasa, Makoto and Uemura, Keisuke and Hakotani, Tomoki and Hashimoto, Masahiro and Yamada, Yoshitake and Yamada, Minoru and Yokoyama, Yoichi and Jinzaki, Masahiro and others},
  journal={Scientific reports},
  volume={15},
  number={1},
  pages={125},
  year={2025},
  publisher={Nature Publishing Group UK London}
}

@article{ovadia2019can,
  title={Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift},
  author={Ovadia, Yaniv and Fertig, Emily and Ren, Jie and Nado, Zachary and Nowozin, Sebastian and Dillon, Joshua and Lakshminarayanan, Balaji and Snoek, Jasper},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{carannante2022self,
  title={Self-assessment and robust anomaly detection with bayesian deep learning},
  author={Carannante, Giuseppina and Dera, Dimah and Aminul, Orune and Bouaynaya, Nidhal C and Rasool, Ghulam},
  booktitle={2022 25th International Conference on Information Fusion (FUSION)},
  pages={1--8},
  year={2022},
  organization={IEEE}
}

@article{wang2019aleatoric,
  title={Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks},
  author={Wang, Guotai and Li, Wenqi and Aertsen, Michael and Deprest, Jan and Ourselin, S{\'e}bastien and Vercauteren, Tom},
  journal={Neurocomputing},
  volume={338},
  pages={34--45},
  year={2019},
  publisher={Elsevier}
}

%%%%
%data

@misc{ma_jun_2020_3757476,
  author       = {Ma, Jun and
                  Ge, Cheng and
                  Wang, Yixin and
                  An, Xingle and
                  Gao, Jiantao and
                  Yu, Ziqi and
                  Zhang, Minqing and
                  Liu, Xin and
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                  Mei, Sen and
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                  Li, Chen and
                  Tian, Lu and
                  Zhu, Yuntao and
                  Zhu, Qiongjie and
                  Dong, Guoqiang and
                  He, Jian},
  title        = {COVID-19 CT Lung and Infection Segmentation Dataset},
  month        = apr,
  year         = {2020},
  publisher    = {Zenodo},
  version      = {Version 1.0},
  doi          = {10.5281/zenodo.3757476}
}


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@inproceedings{ronneberger2015u,
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@inproceedings{kamnitsas2017ensembles,
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@inproceedings{madry2018towards,
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@article{goyal2018noise,
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@article{christin2021going,
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@article{finlayson2019adversarial,
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