@article{zhang2021efficient,
  title={Efficient context-aware network for abdominal multi-organ segmentation},
  author={Zhang, Fan and Wang, Yu and Yang, Hua},
  journal={arXiv preprint arXiv:2109.10601},
  year={2021}
}

@inproceedings{zhu20183d,
  title={A 3D coarse-to-fine framework for volumetric medical image segmentation},
  author={Zhu, Zhuotun and Xia, Yingda and Shen, Wei and Fishman, Elliot and Yuille, Alan},
  booktitle={2018 International conference on 3D vision (3DV)},
  pages={682--690},
  year={2018},
  organization={IEEE}
}

@article{simpson2019large,
  title={A large annotated medical image dataset for the development and evaluation of segmentation algorithms},
  author={Simpson, Amber L and Antonelli, Michela and Bakas, Spyridon and Bilello, Michel and Farahani, Keyvan and Van Ginneken, Bram and Kopp-Schneider, Annette and Landman, Bennett A and Litjens, Geert and Menze, Bjoern and others},
  journal={arXiv preprint arXiv:1902.09063},
  year={2019}
}

@misc{heller2020international,
  title={An international challenge to use artificial intelligence to define the state-of-the-art in kidney and kidney tumor segmentation in CT imaging.},
  author={Heller, Nicholas and McSweeney, Sean and Peterson, Matthew Thomas and Peterson, Sarah and Rickman, Jack and Stai, Bethany and Tejpaul, Resha and Oestreich, Makinna and Blake, Paul and Rosenberg, Joel and others},
  year={2020},
  publisher={American Society of Clinical Oncology}
}

@misc{NIHPancreas,
  title = {{NIH Pancreas}},
  howpublished = "\url{https://wiki.cancerimagingarchive.net/display/Public/Pancreas-CT}",
  year = {2020},
  note = "[Online; Accessed: Aug. 2020]"
}

@inproceedings{ronneberger2015u,
  title={U-net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={International Conference on Medical image computing and computer-assisted intervention},
  pages={234--241},
  year={2015},
  organization={Springer}
}

@article{ma2021abdomenct,
  title={Abdomenct-1k: Is abdominal organ segmentation a solved problem},
  author={Ma, Jun and Zhang, Yao and Gu, Song and Zhu, Cheng and Ge, Cheng and Zhang, Yichi and An, Xingle and Wang, Congcong and Wang, Qiyuan and Liu, Xin and others},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
  publisher={IEEE}
}


@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}
}

@article{ma2021loss,
  title={Loss odyssey in medical image segmentation},
  author={Ma, Jun and Chen, Jianan and Ng, Matthew and Huang, Rui and Li, Yu and Li, Chen and Yang, Xiaoping and Martel, Anne L},
  journal={Medical Image Analysis},
  volume={71},
  pages={102035},
  year={2021},
  publisher={Elsevier}
}

@article{bilic2019liver,
  title={The liver tumor segmentation benchmark (lits)},
  author={Bilic, Patrick and Christ, Patrick Ferdinand and Vorontsov, Eugene and Chlebus, Grzegorz and Chen, Hao and Dou, Qi and Fu, Chi-Wing and Han, Xiao and Heng, Pheng-Ann and Hesser, J{\"u}rgen and others},
  journal={arXiv preprint arXiv:1901.04056},
  year={2019}
}


@article{heller2021state,
  title={The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge},
  author={Heller, Nicholas and Isensee, Fabian and Maier-Hein, Klaus H and Hou, Xiaoshuai and Xie, Chunmei and Li, Fengyi and Nan, Yang and Mu, Guangrui and Lin, Zhiyong and Han, Miofei and others},
  journal={Medical image analysis},
  volume={67},
  pages={101821},
  year={2021},
  publisher={Elsevier}
}

@misc{roth9data,
  title={Data from Pancreas-CT. The Cancer Imaging Archive (2016)},
  author={Roth, H and Farag, A and Turkbey, EB and Lu, L and Liu, J and Summers, RM}
}

@inproceedings{roth2015deeporgan,
  title={Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation},
  author={Roth, Holger R and Lu, Le and Farag, Amal and Shin, Hoo-Chang and Liu, Jiamin and Turkbey, Evrim B and Summers, Ronald M},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={556--564},
  year={2015},
  organization={Springer}
}

@article{clark2013cancer,
  title={The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository},
  author={Clark, Kenneth and Vendt, Bruce and Smith, Kirk and Freymann, John and Kirby, Justin and Koppel, Paul and Moore, Stephen and Phillips, Stanley and Maffitt, David and Pringle, Michael and others},
  journal={Journal of digital imaging},
  volume={26},
  number={6},
  pages={1045--1057},
  year={2013},
  publisher={Springer}
}

@inproceedings{he2015delving,
  title={Delving deep into rectifiers: Surpassing human-level performance on imagenet classification},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={1026--1034},
  year={2015}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

@article{sohn2020fixmatch,
  title={Fixmatch: Simplifying semi-supervised learning with consistency and confidence},
  author={Sohn, Kihyuk and Berthelot, David and Carlini, Nicholas and Zhang, Zizhao and Zhang, Han and Raffel, Colin A and Cubuk, Ekin Dogus and Kurakin, Alexey and Li, Chun-Liang},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={596--608},
  year={2020}
}

@article{isensee2019automated,
  title={Automated design of deep learning methods for biomedical image segmentation},
  author={Isensee, Fabian and J{\"a}ger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={arXiv preprint arXiv:1904.08128},
  year={2019}
}

@inproceedings{nie2018asdnet,
  title={ASDNet: attention based semi-supervised deep networks for medical image segmentation},
  author={Nie, Dong and Gao, Yaozong and Wang, Li and Shen, Dinggang},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={370--378},
  year={2018},
  organization={Springer}
}

@inproceedings{bortsova2019semi,
  title={Semi-supervised medical image segmentation via learning consistency under transformations},
  author={Bortsova, Gerda and Dubost, Florian and Hogeweg, Laurens and Katramados, Ioannis and Bruijne, Marleen de},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={810--818},
  year={2019},
  organization={Springer}
}

@inproceedings{li2020self,
  title={Self-loop uncertainty: A novel pseudo-label for semi-supervised medical image segmentation},
  author={Li, Yuexiang and Chen, Jiawei and Xie, Xinpeng and Ma, Kai and Zheng, Yefeng},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={614--623},
  year={2020},
  organization={Springer}
}

@inproceedings{bai2017semi,
  title={Semi-supervised learning for network-based cardiac MR image segmentation},
  author={Bai, Wenjia and Oktay, Ozan and Sinclair, Matthew and Suzuki, Hideaki and Rajchl, Martin and Tarroni, Giacomo and Glocker, Ben and King, Andrew and Matthews, Paul M and Rueckert, Daniel},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={253--260},
  year={2017},
  organization={Springer}
}

@article{upretee2022fixmatchseg,
    title={FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation},
    author={Upretee, Pratima and Khanal, Bishesh},
    journal={arXiv preprint arXiv:2208.00400},
    year={2022}
}

@inproceedings{milletari2016v,
  title={V-net: Fully convolutional neural networks for volumetric medical image segmentation},
  author={Milletari, Fausto and Navab, Nassir and Ahmadi, Seyed-Ahmad},
  booktitle={2016 fourth international conference on 3D vision (3DV)},
  pages={565--571},
  year={2016},
  organization={IEEE}
}

@article{ulyanov2016instance,
  title={Instance normalization: The missing ingredient for fast stylization},
  author={Ulyanov, Dmitry and Vedaldi, Andrea and Lempitsky, Victor},
  journal={arXiv preprint arXiv:1607.08022},
  year={2016}
}

@article{li2020transformation,
  title={Transformation-consistent self-ensembling model for semisupervised medical image segmentation},
  author={Li, Xiaomeng and Yu, Lequan and Chen, Hao and Fu, Chi-Wing and Xing, Lei and Heng, Pheng-Ann},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  volume={32},
  number={2},
  pages={523--534},
  year={2020},
  publisher={IEEE}
}

@inproceedings{baur2017semi,
  title={Semi-supervised deep learning for fully convolutional networks},
  author={Baur, Christoph and Albarqouni, Shadi and Navab, Nassir},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={311--319},
  year={2017},
  organization={Springer}
}

@inproceedings{cui2019semi,
  title={Semi-supervised brain lesion segmentation with an adapted mean teacher model},
  author={Cui, Wenhui and Liu, Yanlin and Li, Yuxing and Guo, Menghao and Li, Yiming and Li, Xiuli and Wang, Tianle and Zeng, Xiangzhu and Ye, Chuyang},
  booktitle={International Conference on Information Processing in Medical Imaging},
  pages={554--565},
  year={2019},
  organization={Springer}
}

@inproceedings{yu2019uncertainty,
  title={Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation},
  author={Yu, Lequan and Wang, Shujun and Li, Xiaomeng and Fu, Chi-Wing and Heng, Pheng-Ann},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={605--613},
  year={2019},
  organization={Springer}
}

@article{ouali2020overview,
  title={An overview of deep semi-supervised learning},
  author={Ouali, Yassine and Hudelot, C{\'e}line and Tami, Myriam},
  journal={arXiv preprint arXiv:2006.05278},
  year={2020}
}

@inproceedings{sedai2017semi,
  title={Semi-supervised segmentation of optic cup in retinal fundus images using variational autoencoder},
  author={Sedai, Suman and Mahapatra, Dwarikanath and Hewavitharanage, Sajini and Maetschke, Stefan and Garnavi, Rahil},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={75--82},
  year={2017},
  organization={Springer}
}

@inproceedings{zhang2017deep,
  title={Deep adversarial networks for biomedical image segmentation utilizing unannotated images},
  author={Zhang, Yizhe and Yang, Lin and Chen, Jianxu and Fredericksen, Maridel and Hughes, David P and Chen, Danny Z},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={408--416},
  year={2017},
  organization={Springer}
}

@inproceedings{chartsias2018factorised,
  title={Factorised spatial representation learning: Application in semi-supervised myocardial segmentation},
  author={Chartsias, Agisilaos and Joyce, Thomas and Papanastasiou, Giorgos and Semple, Scott and Williams, Michelle and Newby, David and Dharmakumar, Rohan and Tsaftaris, Sotirios A},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={490--498},
  year={2018},
  organization={Springer}
}

@inproceedings{ganaye2018semi,
  title={Semi-supervised learning for segmentation under semantic constraint},
  author={Ganaye, Pierre-Antoine and Sdika, Micha{\"e}l and Benoit-Cattin, Hugues},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={595--602},
  year={2018},
  organization={Springer}
}