@book{Lamport:Book:1989,
 author = {Lamport, Leslie},
 title = {Latex: A Document Preparation System},
 year = {1986},
 isbn = {0-201-15790-X},
 publisher = {Addison-Wesley Longman Publishing Co., Inc.},
 address = {Boston, MA, USA},
}

%% rebuttal
@article{peng2020discretely,
	title={Discretely-constrained deep network for weakly supervised segmentation},
	author={Peng, Jizong and Kervadec, Hoel and Dolz, Jose and Ayed, Ismail Ben and Pedersoli, Marco and Desrosiers, Christian},
	journal={Neural Networks},
	volume={130},
	pages={297--308},
	year={2020},
	publisher={Elsevier}
}

@article{oktay2017anatomically,
	title={Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation},
	author={Oktay, Ozan and Ferrante, Enzo and Kamnitsas, Konstantinos and Heinrich, Mattias and Bai, Wenjia and Caballero, Jose and Cook, Stuart A and De Marvao, Antonio and Dawes, Timothy and O‘Regan, Declan P and others},
	journal={IEEE transactions on medical imaging},
	volume={37},
	number={2},
	pages={384--395},
	year={2017},
	publisher={IEEE}
}

@inproceedings{baumgartner2017exploration,
	title={An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation},
	author={Baumgartner, Christian F and Koch, Lisa M and Pollefeys, Marc and Konukoglu, Ender},
	booktitle={International Workshop on Statistical Atlases and Computational Models of the Heart},
	pages={111--119},
	year={2017},
	organization={Springer}
}


%% autoencoder
@InProceedings{Sundermeyer_2018_ECCV,
	author = {Sundermeyer, Martin and Marton, Zoltan-Csaba and Durner, Maximilian and Brucker, Manuel and Triebel, Rudolph},
	title = {Implicit 3D Orientation Learning for 6D Object Detection from RGB Images},
	booktitle = {ECCV},
	year = {2018}
}

@article{vincent2010stacked,
	title={Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.},
	author={Vincent, Pascal and Larochelle, Hugo and Lajoie, Isabelle and Bengio, Yoshua and Manzagol, Pierre-Antoine and Bottou, L{\'e}on},
	journal={JMLR},
	year={2010}
}

%% SOTA
@inproceedings{tang2018regularized,
	title={On regularized losses for weakly-supervised cnn segmentation},
	author={Tang, Meng and Perazzi, Federico and Djelouah, Abdelaziz and Ben Ayed, Ismail and Schroers, Christopher and Boykov, Yuri},
	booktitle={ECCV},
	year={2018}
}

@inproceedings{kervadec2020bounding,
	title={Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision},
	author={Kervadec, Hoel and Dolz, Jose and Wang, Shanshan and Granger, Eric and ben Ayed, Ismail},
	booktitle={MIDL},
	year={2020}
}

@article{isensee2019automated_old,
	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}
}

%%Isensee2020nnUNetAS,
@article{isensee2019automated,
	title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.},
	author={Fabian Isensee and Paul F. Jaeger and Simon A. A. Kohl and J. Petersen and Klaus Maier-Hein},
	journal={Nature methods},
	year={2020}
}

% Dataset
@article{litjens2014evaluation,
	title={Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge},
	author={Litjens, Geert and Toth, Robert and van de Ven, Wendy and Hoeks, Caroline and Kerkstra, Sjoerd and van Ginneken, Bram and Vincent, Graham and Guillard, Gwenael and Birbeck, Neil and Zhang, Jindang and others},
	journal={Medical image analysis},
	year={2014},
}

@article{trullo2019multiorgan,
	title={Multiorgan segmentation using distance-aware adversarial networks},
	author={Trullo, Roger and Petitjean, Caroline and Dubray, Bernard and Ruan, Su},
	journal={Journal of Medical Imaging},
	year={2019},
}

@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={MICCAI},
	year={2019},
}

@misc{pascal-voc-2012,
	author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.",
	title = "The {PASCAL} {V}isual {O}bject {C}lasses {C}hallenge 2012 {(VOC2012)} {R}esults",
	howpublished = "http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html"
}

@article{everingham2010pascal,
	title={The pascal visual object classes (voc) challenge},
	author={Everingham, Mark and Van Gool, Luc and Williams, Christopher KI and Winn, John and Zisserman, Andrew},
	journal={IJCV},
	year={2010}
}


% weak medical

@article{kervadec2019constrained,
	title={Constrained-CNN losses for weakly supervised segmentation},
	author={Kervadec, Hoel and Dolz, Jose and Tang, Meng and Granger, Eric and Boykov, Yuri and Ayed, Ismail Ben},
	journal={Medical image analysis},
	year={2019},
}



% related: self-taught learning
@inproceedings{raina2007self,
	title={Self-taught learning: transfer learning from unlabeled data},
	author={Raina, Rajat and Battle, Alexis and Lee, Honglak and Packer, Benjamin and Ng, Andrew Y},
	booktitle={ICML},
	year={2007}
}

@inproceedings{wang2013robust,
	title={Robust and discriminative self-taught learning},
	author={Wang, Hua and Nie, Feiping and Huang, Heng},
	booktitle={ICML},
	year={2013}
}

@article{li2017self,
	title={Self-taught low-rank coding for visual learning},
	author={Li, Sheng and Li, Kang and Fu, Yun},
	journal={NeurIPS},
	year={2017},
}

@inproceedings{dai2008self,
	title={Self-taught clustering},
	author={Dai, Wenyuan and Yang, Qiang and Xue, Gui-Rong and Yu, Yong},
	booktitle={ICML},
	year={2008}
}

@article{kuen2015self,
	title={Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle},
	author={Kuen, Jason and Lim, Kian Ming and Lee, Chin Poo},
	journal={Pattern recognition},
	year={2015},
}

@inproceedings{bazzani2016self,
	title={Self-taught object localization with deep networks},
	author={Bazzani, Loris and Bergamo, Alessandra and Anguelov, Dragomir and Torresani, Lorenzo},
	booktitle={WACV},
	year={2016},
}

@inproceedings{jie2017deep,
	title={Deep self-taught learning for weakly supervised object localization},
	author={Jie, Zequn and Wei, Yunchao and Jin, Xiaojie and Feng, Jiashi and Liu, Wei},
	booktitle={CVPR},
	year={2017}
}

@article{feng2020autoencoder,
	title={Autoencoder based sample selection for self-taught learning},
	author={Feng, Siwei and Yu, Han and Duarte, Marco F},
	journal={Knowledge-Based Systems},
	year={2020},
}

% related: wsss
% wsss: image
@inproceedings{wang2020self,
	title={Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation},
	author={Wang, Yude and Zhang, Jie and Kan, Meina and Shan, Shiguang and Chen, Xilin},
	booktitle={CVPR},
	year={2020}
}

@inproceedings{fan2020learning,
	title={Learning Integral Objects With Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation},
	author={Fan, Junsong and Zhang, Zhaoxiang and Song, Chunfeng and Tan, Tieniu},
	booktitle={CVPR},
	year={2020}
}

@inproceedings{chang2020weakly,
	title={Weakly-Supervised Semantic Segmentation via Sub-Category Exploration},
	author={Chang, Yu-Ting and Wang, Qiaosong and Hung, Wei-Chih and Piramuthu, Robinson and Tsai, Yi-Hsuan and Yang, Ming-Hsuan},
	booktitle={CVPR},
	year={2020}
}

@article{sun2020mining,
	title={Mining cross-image semantics for weakly supervised semantic segmentation},
	author={Sun, Guolei and Wang, Wenguan and Dai, Jifeng and Van Gool, Luc},
	journal={arXiv preprint arXiv:2007.01947},
	year={2020}
}

@inproceedings{kolesnikov2016seed,
	title={Seed, expand and constrain: Three principles for weakly-supervised image segmentation},
	author={Kolesnikov, Alexander and Lampert, Christoph H},
	booktitle={ECCV},
	year={2016},
}

@inproceedings{huang2018weakly,
	title={Weakly-supervised semantic segmentation network with deep seeded region growing},
	author={Huang, Zilong and Wang, Xinggang and Wang, Jiasi and Liu, Wenyu and Wang, Jingdong},
	booktitle={CVPR},
	year={2018}
}

@inproceedings{wang2018weakly,
	title={Weakly-supervised semantic segmentation by iteratively mining common object features},
	author={Wang, Xiang and You, Shaodi and Li, Xi and Ma, Huimin},
	booktitle={CVPR},
	year={2018}
}

% wsss: box
@inproceedings{dai2015boxsup,
	title={Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation},
	author={Dai, Jifeng and He, Kaiming and Sun, Jian},
	booktitle={ICCV},
	year={2015}
}

@inproceedings{khoreva2017simple,
	title={Simple does it: Weakly supervised instance and semantic segmentation},
	author={Khoreva, Anna and Benenson, Rodrigo and Hosang, Jan and Hein, Matthias and Schiele, Bernt},
	booktitle={CVPR},
	year={2017}
}

@inproceedings{song2019box,
	title={Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation},
	author={Song, Chunfeng and Huang, Yan and Ouyang, Wanli and Wang, Liang},
	booktitle={CVPR},
	year={2019}
}

@inproceedings{papandreou2015weakly,
	title={Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation},
	author={Papandreou, George and Chen, Liang-Chieh and Murphy, Kevin P and Yuille, Alan L},
	booktitle={ICCV},
	year={2015}
}

% wsss: scribble
@inproceedings{lin2016scribblesup,
	title={Scribblesup: Scribble-supervised convolutional networks for semantic segmentation},
	author={Lin, Di and Dai, Jifeng and Jia, Jiaya and He, Kaiming and Sun, Jian},
	booktitle={CVPR},
	year={2016}
}

@inproceedings{vernaza2017learning,
	title={Learning random-walk label propagation for weakly-supervised semantic segmentation},
	author={Vernaza, Paul and Chandraker, Manmohan},
	booktitle={CVPR},
	year={2017}
}

% wsss: point
@inproceedings{maninis2018deep,
	title={Deep extreme cut: From extreme points to object segmentation},
	author={Maninis, Kevis-Kokitsi and Caelles, Sergi and Pont-Tuset, Jordi and Van Gool, Luc},
	booktitle={CVPR},
	year={2018}
}

@inproceedings{bearman2016s,
	title={What’s the point: Semantic segmentation with point supervision},
	author={Bearman, Amy and Russakovsky, Olga and Ferrari, Vittorio and Fei-Fei, Li},
	booktitle={ECCV},
	year={2016},
}

@inproceedings{papadopoulos2017extreme,
	title={Extreme clicking for efficient object annotation},
	author={Papadopoulos, Dim P and Uijlings, Jasper RR and Keller, Frank and Ferrari, Vittorio},
	booktitle={ICCV},
	year={2017}
}

% wsss: medical
@article{rajchl2016deepcut,
	title={Deepcut: Object segmentation from bounding box annotations using convolutional neural networks},
	author={Rajchl, Martin and Lee, Matthew CH and Oktay, Ozan and Kamnitsas, Konstantinos and Passerat-Palmbach, Jonathan and Bai, Wenjia and Damodaram, Mellisa and Rutherford, Mary A and Hajnal, Joseph V and Kainz, Bernhard and others},
	journal={IEEE TMI},
	year={2016},
}

@inproceedings{cai2018accurate,
	title={Accurate weakly-supervised deep lesion segmentation using large-scale clinical annotations: Slice-propagated 3d mask generation from 2d recist},
	author={Cai, Jinzheng and Tang, Youbao and Lu, Le and Harrison, Adam P and Yan, Ke and Xiao, Jing and Yang, Lin and Summers, Ronald M},
	booktitle={MICCAI},
	year={2018},
}

@incollection{can2018learning,
	title={Learning to segment medical images with scribble-supervision alone},
	author={Can, Yigit B and Chaitanya, Krishna and Mustafa, Basil and Koch, Lisa M and Konukoglu, Ender and Baumgartner, Christian F},
	booktitle={Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support},
	year={2018},
}

@inproceedings{ji2019scribble,
	title={Scribble-based hierarchical weakly supervised learning for brain tumor segmentation},
	author={Ji, Zhanghexuan and Shen, Yan and Ma, Chunwei and Gao, Mingchen},
	booktitle={MICCAI},
	year={2019},
}


% wsss: constraint
@inproceedings{pathak2015constrained,
	title={Constrained convolutional neural networks for weakly supervised segmentation},
	author={Pathak, Deepak and Krahenbuhl, Philipp and Darrell, Trevor},
	booktitle={ICCV},
	year={2015}
}

% related: denoising autoencoder
@inproceedings{vincent2008extracting,
	title={Extracting and composing robust features with denoising autoencoders},
	author={Vincent, Pascal and Larochelle, Hugo and Bengio, Yoshua and Manzagol, Pierre-Antoine},
	booktitle={ICML},
	year={2008}
}

@article{geras2014scheduled,
	title={Scheduled denoising autoencoders},
	author={Geras, Krzysztof J and Sutton, Charles},
	journal={arXiv preprint arXiv:1406.3269},
	year={2014}
}

@inproceedings{xiong2016denoising,
	title={Denoising auto-encoders toward robust unsupervised feature representation},
	author={Xiong, Wei and Du, Bo and Zhang, Lefei and Zhang, Liangpei and Tao, Dacheng},
	booktitle={IJCNN},
	year={2016},
}

@inproceedings{gondara2016medical,
	title={Medical image denoising using convolutional denoising autoencoders},
	author={Gondara, Lovedeep},
	booktitle={ICDMW},
	year={2016},
}

% intro_add
@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={3DV},
	year={2016},
}

@inproceedings{pinheiro2015image,
	title={From image-level to pixel-level labeling with convolutional networks},
	author={Pinheiro, Pedro O and Collobert, Ronan},
	booktitle={CVPR},
	year={2015}
}

@article{rother2004grabcut,
  title={" GrabCut" interactive foreground extraction using iterated graph cuts},
  author={Rother, Carsten and Kolmogorov, Vladimir and Blake, Andrew},
  journal={ACM transactions on graphics (TOG)},
  year={2004},
  publisher={ACM New York, NY, USA}
}
