@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})



@misc{Authors14,
 author = {FirstName LastName},
 title = {The frobnicatable foo filter},
 note = {Face and Gesture submission ID 324. Supplied as supplemental material {\tt fg324.pdf}},
 year = 2014
}

% UNets
@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}}

@inproceedings{cicek20163d,
title={3D U-Net: learning dense volumetric segmentation from sparse annotation},
author={Çiçek, {"O}zg{"u}n and Abdulkadir, Ahmed and Lienkamp, Soeren S and Brox, Thomas and Ronneberger, Olaf},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={424--432},
year={2016},
organization={Springer}}

@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}}

@inproceedings{zhou2018unet,
title={UNet++: A Nested U-Net Architecture for Medical Image Segmentation},
author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},
booktitle={Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support},
pages={3--11},
year={2018},
organization={Springer}}

@article{isensee2021nnu,
title={nnU-Net: a self-configuring method for deep learning-based biomedical 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},
pages={203--211},
year={2021},
publisher={Nature Publishing Group}}

@inproceedings{xu2022auto,
  title={Auto-segmentation of Hip Joints Using MultiPlanar UNet with Transfer Learning},
  author={Xu, Peidi and Moshfeghifar, Faezeh and Gholamalizadeh, Torkan and Nielsen, Michael Bachmann and Erleben, Kenny and Darkner, Sune},
  booktitle={Medical Image Learning with Limited and Noisy Data: First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings},
  pages={153--162},
  year={2022},
  organization={Springer}
}

@article{renard2020variability,
  title={Variability and reproducibility in deep learning for medical image segmentation},
  author={Renard, F{\'e}lix and Guedria, Soulaimane and Palma, Noel De and Vuillerme, Nicolas},
  journal={Scientific Reports},
  volume={10},
  number={1},
  pages={1--16},
  year={2020},
  publisher={Springer}
}
% semi papers

@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={Medical Image Computing and Computer Assisted Intervention--MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13--17, 2019, Proceedings, Part II 22},
  pages={605--613},
  year={2019},
  organization={Springer}
}

@inproceedings{wang2022semi,
  title={Semi-supervised semantic segmentation using unreliable pseudo-labels},
  author={Wang, Yuchao and Wang, Haochen and Shen, Yujun and Fei, Jingjing and Li, Wei and Jin, Guoqiang and Wu, Liwei and Zhao, Rui and Le, Xinyi},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4248--4257},
  year={2022}
}

@article{tarvainen2017mean,
  title={Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results},
  author={Tarvainen, Antti and Valpola, Harri},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@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={Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20},
  pages={408--416},
  year={2017},
  organization={Springer}
}

@article{verma2022interpolation,
  title={Interpolation consistency training for semi-supervised learning},
  author={Verma, Vikas and Kawaguchi, Kenji and Lamb, Alex and Kannala, Juho and Solin, Arno and Bengio, Yoshua and Lopez-Paz, David},
  journal={Neural Networks},
  volume={145},
  pages={90--106},
  year={2022},
  publisher={Elsevier}
}

@inproceedings{chen2021semi,
  title={Semi-supervised semantic segmentation with cross pseudo supervision},
  author={Chen, Xiaokang and Yuan, Yuhui and Zeng, Gang and Wang, Jingdong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2613--2622},
  year={2021}
}


@article{xie2020unsupervised,
  title={Unsupervised data augmentation for consistency training},
  author={Xie, Qizhe and Dai, Zihang and Hovy, Eduard and Luong, Thang and Le, Quoc},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={6256--6268},
  year={2020}
}

@article{miyato2018virtual,
  title={Virtual adversarial training: a regularization method for supervised and semi-supervised learning},
  author={Miyato, Takeru and Maeda, Shin-ichi and Koyama, Masanori and Ishii, Shin},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={41},
  number={8},
  pages={1979--1993},
  year={2018},
  publisher={IEEE}
}


@inproceedings{liu2022perturbed,
  title={Perturbed and strict mean teachers for semi-supervised semantic segmentation},
  author={Liu, Yuyuan and Tian, Yu and Chen, Yuanhong and Liu, Fengbei and Belagiannis, Vasileios and Carneiro, Gustavo},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4258--4267},
  year={2022}
}

@article{yang2022revisiting,
  title={Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation},
  author={Yang, Lihe and Qi, Lei and Feng, Litong and Zhang, Wayne and Shi, Yinghuan},
  journal={arXiv preprint arXiv:2208.09910},
  year={2022}
}

@inproceedings{xu2021dash,
  title={Dash: Semi-supervised learning with dynamic thresholding},
  author={Xu, Yi and Shang, Lei and Ye, Jinxing and Qian, Qi and Li, Yu-Feng and Sun, Baigui and Li, Hao and Jin, Rong},
  booktitle={International Conference on Machine Learning},
  pages={11525--11536},
  year={2021},
  organization={PMLR}
}

@inproceedings{yang2022st,
  title={St++: Make self-training work better for semi-supervised semantic segmentation},
  author={Yang, Lihe and Zhuo, Wei and Qi, Lei and Shi, Yinghuan and Gao, Yang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4268--4277},
  year={2022}
}

@inproceedings{ouali2020semi,
  title={Semi-supervised semantic segmentation with cross-consistency training},
  author={Ouali, Yassine and Hudelot, C{\'e}line and Tami, Myriam},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12674--12684},
  year={2020}
}

@article{van2020survey,
  title={A survey on semi-supervised learning},
  author={Van Engelen, Jesper E and Hoos, Holger H},
  journal={Machine learning},
  volume={109},
  number={2},
  pages={373--440},
  year={2020},
  publisher={Springer}
}

 @inproceedings{luo2021ctbct,
  title={Semi-supervised medical image segmentation via cross teaching between cnn and transformer},
  author={Luo, Xiangde and Hu, Minhao and Song, Tao and Wang, Guotai and Zhang, Shaoting},
  booktitle={International Conference on Medical Imaging with Deep Learning},
  pages={820--833},
  year={2022},
  organization={PMLR}
}

@inproceedings{chen2019multi,
  title={Multi-task attention-based semi-supervised learning for medical image segmentation},
  author={Chen, Shuai and Bortsova, Gerda and Garc{\'\i}a-Uceda Ju{\'a}rez, Antonio and Van Tulder, Gijs and De Bruijne, Marleen},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13--17, 2019, Proceedings, Part III 22},
  pages={457--465},
  year={2019},
  organization={Springer}
}


@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{chen2020improved,
  title={Improved baselines with momentum contrastive learning},
  author={Chen, Xinlei and Fan, Haoqi and Girshick, Ross and He, Kaiming},
  journal={arXiv preprint arXiv:2003.04297},
  year={2020}
}

@inproceedings{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={International conference on machine learning},
  pages={1597--1607},
  year={2020},
  organization={PMLR}
}

@inproceedings{he2020momentum,
  title={Momentum contrast for unsupervised visual representation learning},
  author={He, Kaiming and Fan, Haoqi and Wu, Yuxin and Xie, Saining and Girshick, Ross},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={9729--9738},
  year={2020}
}

@article{ito2019semi,
  title={Semi-supervised deep learning of brain tissue segmentation},
  author={Ito, Ryo and Nakae, Ken and Hata, Junichi and Okano, Hideyuki and Ishii, Shin},
  journal={Neural Networks},
  volume={116},
  pages={25--34},
  year={2019},
  publisher={Elsevier}
}

@article{laine2016temporal,
  title={Temporal ensembling for semi-supervised learning},
  author={Laine, Samuli and Aila, Timo},
  journal={arXiv preprint arXiv:1610.02242},
  year={2016}
}

@article{kendall2017uncertainties,
  title={What uncertainties do we need in bayesian deep learning for computer vision?},
  author={Kendall, Alex and Gal, Yarin},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@inproceedings{vu2019advent,
  title={Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation},
  author={Vu, Tuan-Hung and Jain, Himalaya and Bucher, Maxime and Cord, Matthieu and P{\'e}rez, Patrick},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2517--2526},
  year={2019}
}

@article{cootes2001active,
  title={Active appearance models},
  author={Cootes, Timothy F. and Edwards, Gareth J. and Taylor, Christopher J.},
  journal={IEEE Transactions on pattern analysis and machine intelligence},
  volume={23},
  number={6},
  pages={681--685},
  year={2001},
  publisher={IEEE}
}

@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}
}

@inproceedings{luo2021semi,
  title={Semi-supervised medical image segmentation through dual-task consistency},
  author={Luo, Xiangde and Chen, Jieneng and Song, Tao and Wang, Guotai},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={35},
  number={10},
  pages={8801--8809},
  year={2021}
}