@article{litjens2017survey,
  title={A survey on deep learning in medical image analysis},
  author={Litjens, Geert and Kooi, Thijs and Bejnordi, Babak Ehteshami and Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Ghafoorian, Mohsen and Van Der Laak, Jeroen Awm and Van Ginneken, Bram and S{\'a}nchez, Clara I},
  journal={Medical Image Analysis},
  volume={42},
  pages={60--88},
  year={2017},
  publisher={Elsevier}
}

@article{hosseini2020tried,
  title={I tried a bunch of things: The dangers of unexpected overfitting in classification of brain data},
  author={Hosseini, Mahan and Powell, Michael and Collins, John and Callahan-Flintoft, Chloe and Jones, William and Bowman, Howard and Wyble, Brad},
  journal={Neuroscience \& Biobehavioral Reviews},
  volume={119},
  pages={456--467},
  year={2020},
  publisher={Elsevier}
}

@book{hutter2019automated,
  title={Automated Machine Learning: Methods, Systems, Challenges},
  author={Hutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin},
  year={2019},
  publisher={Springer Nature}
}

@article{shen2017deep,
  title={Deep learning in medical image analysis},
  author={Shen, Dinggang and Wu, Guorong and Suk, Heung-Il},
  journal={Annual Review of Biomedical Engineering},
  volume={19},
  number={1},
  pages={221--248},
  year={2017},
  publisher={Annual Reviews}
}

@article{yang2020hyperparameter,
  title={On hyperparameter optimization of machine learning algorithms: Theory and practice},
  author={Yang, Li and Shami, Abdallah},
  journal={Neurocomputing},
  volume={415},
  pages={295--316},
  year={2020},
  publisher={Elsevier}
}

@article{li2018hyperband,
  title={{Hyperband}: A novel bandit-based approach to hyperparameter optimization},
  author={Li, Lisha and Jamieson, Kevin and DeSalvo, Giulia and Rostamizadeh, Afshin and Talwalkar, Ameet},
  journal={Journal of Machine Learning Research},
  volume={18},
  number={185},
  pages={1--52},
  year={2018}
}

@inproceedings{zhou2016learning,
  title={Learning deep features for discriminative localization},
  author={Zhou, Bolei and Khosla, Aditya and Lapedriza, Agata and Oliva, Aude and Torralba, Antonio},
  booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
  pages={2921--2929},
  year={2016}
}

@article{becktepe2025auto,
  title={{Auto-nnU-Net}: Towards Automated Medical Image Segmentation},
  author={Becktepe, Jannis and Hennig, Leona and Oeltze-Jafra, Steffen and Lindauer, Marius},
  journal={arXiv preprint arXiv:2505.16561},
  year={2025}
}

@article{elangovan2024comparative,
  title={A comparative study of an on premise AutoML solution for medical image classification},
  author={Elangovan, Kabilan and Lim, Gilbert and Ting, Daniel},
  journal={Scientific Reports},
  volume={14},
  number={1},
  pages={10483},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

@incollection{jidney2023automl,
  title={Automl systems for medical imaging},
  author={Jidney, Tasmia Tahmida and Biswas, Angona and Abdullah Al, Nasim Md and Hossain, Ismail and Alam, Md Jahangir and Talukder, Sajedul and Hossain, Mofazzal and Ullah, Md Azim},
  booktitle={Data driven approaches on medical imaging},
  pages={91--106},
  year={2023},
  publisher={Springer}
}

@article{ali2024review,
  title={A review of AutoML optimization techniques for medical image applications},
  author={Ali, Muhammad Junaid and Essaid, Mokhtar and Moalic, Laurent and Idoumghar, Lhassane},
  journal={Computerized Medical Imaging and Graphics},
  volume={118},
  pages={102441},
  year={2024},
  publisher={Elsevier}
}

@article{awad2021dehb,
  title={{DEHB}: Evolutionary hyperband for scalable, robust and efficient hyperparameter optimization},
  author={Awad, Noor and Mallik, Neeratyoy and Hutter, Frank},
  journal={arXiv preprint arXiv:2105.09821},
  year={2021}
}

@book{price2005differential,
  title={Differential evolution: a practical approach to global optimization},
  author={Price, Kenneth V and Storn, Rainer M and Lampinen, Jouni A},
  year={2005},
  publisher={Springer}
}

@article{isensee2021nnu,
  title={{nnU-Net}: a self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={Nature methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021},
  publisher={Nature Publishing Group}
}

@inproceedings{de2021effect,
  title={The effect of preprocessing on convolutional neural networks for medical image segmentation},
  author={De Raad, KB and van Garderen, Karin A and Smits, Marion and van der Voort, Sebastian R and Incekara, Fatih and Oei, Edwin HG and Hirvasniemi, Jukka and Klein, Stefan and Starmans, Martijn PA},
  booktitle={2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  pages={655--658},
  year={2021},
  organization={IEEE}
}

@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4700--4708},
  year={2017}
}

@article{zhou2022dense,
  title={Dense convolutional network and its application in medical image analysis},
  author={Zhou, Tao and Ye, XinYu and Lu, HuiLing and Zheng, Xiaomin and Qiu, Shi and Liu, YunCan},
  journal={BioMed Research International},
  volume={2022},
  number={1},
  pages={2384830},
  year={2022},
  publisher={Wiley Online Library}
}

@article{rajaraman2021novel,
  title={Novel loss functions for ensemble-based medical image classification},
  author={Rajaraman, Sivaramakrishnan and Zamzmi, Ghada and Antani, Sameer K},
  journal={Plos one},
  volume={16},
  number={12},
  pages={e0261307},
  year={2021},
  publisher={Public Library of Science San Francisco, CA USA}
}

@inproceedings{lee2023improving,
  title={Improving multi-fidelity optimization with a recurring learning rate for hyperparameter tuning},
  author={Lee, HyunJae and Lee, Gihyeon and Kim, Junhwan and Cho, Sungjun and Kim, Dohyun and Yoo, Donggeun},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={2309--2318},
  year={2023}
}

@article{esteva2019guide,
  title={A guide to deep learning in healthcare},
  author={Esteva, Andre and Robicquet, Alexandre and Ramsundar, Bharath and Kuleshov, Volodymyr and DePristo, Mark and Chou, Katherine and Cui, Claire and Corrado, Greg and Thrun, Sebastian and Dean, Jeff},
  journal={Nature medicine},
  volume={25},
  number={1},
  pages={24--29},
  year={2019},
  publisher={Nature Publishing Group US New York}
}

@inproceedings{al2020multi,
  title={Multi-tree genetic programming for feature construction-based domain adaptation in symbolic regression with incomplete data},
  author={Al-Helali, Baligh and Chen, Qi and Xue, Bing and Zhang, Mengjie},
  booktitle={Proceedings of the 2020 Genetic and Evolutionary Computation Conference},
  pages={913--921},
  year={2020}
}

@article{starmans2021worc,
  title={The {WORC} database: {MRI} and {CT} scans, segmentations, and clinical labels for 930 patients from six radiomics studies},
  author={Starmans, Martijn PA and Timbergen, Milea JM and Vos, Melissa and Padmos, Guillaume A and Gr{\"u}nhagen, Dirk J and Verhoef, Cornelis and Sleijfer, Stefan and van Leenders, Geert JLH and Buisman, Florian E and Willemssen, Francois EJA and others},
  journal={medRxiv},
  pages={2021--08},
  year={2021},
  publisher={Cold Spring Harbor Laboratory Press}
}

@article{starmans2021reproducible,
  title={Reproducible radiomics through automated machine learning validated on twelve clinical applications},
  author={Starmans, Martijn and van der Voort, Sebastian R and Phil, Thomas and Timbergen, Milea JM and Vos, Melissa and Padmos, Guillaume A and Kessels, Wouter and Hanff, David and Grunhagen, Dirk J and Verhoef, Cornelis and others},
  journal={arXiv preprint arXiv:2108.08618},
  year={2021}
}

@article{cardoso2022monai,
  title={Monai: An open-source framework for deep learning in healthcare},
  author={Cardoso, M Jorge and Li, Wenqi and Brown, Richard and Ma, Nic and Kerfoot, Eric and Wang, Yiheng and Murrey, Benjamin and Myronenko, Andriy and Zhao, Can and Yang, Dong and others},
  journal={arXiv preprint arXiv:2211.02701},
  year={2022}
}


@article{fawcett2004roc,
  title={ROC graphs: Notes and practical considerations for researchers},
  author={Fawcett, Tom},
  journal={Machine learning},
  volume={31},
  number={1},
  pages={1--38},
  year={2004}
}

@inproceedings{selvan2023operating,
  title={Operating critical machine learning models in resource constrained regimes},
  author={Selvan, Raghavendra and Sch{\"o}n, Julian and Dam, Erik B},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={325--335},
  year={2023},
  organization={Springer}
}