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  title     = {EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
  booktitle = ICML,
  year      = {2019},
}

@article{Zhong2017,
  author    = {Zhong, Zhun and Zheng, Liang and Kang, Guoliang and Li, Shaozi and Yang, Yi},
  title     = {Random erasing data augmentation},
  journal   = {arXiv preprint arXiv:1708.04896},
  year      = {2017},
}

@article{Devries2017,
  author    = {DeVries, Terrance and Taylor, Graham W},
  title     = {Improved regularization of convolutional neural networks with cutout},
  journal   = {arXiv preprint arXiv:1708.04552},
  year      = {2017},
}

@inproceedings{yun2019cutmix,
  title     = {CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features},
  author    = {Yun, Sangdoo and Han, Dongyoon and Oh, Seong Joon and Chun, Sanghyuk and Choe, Junsuk and Yoo, Youngjoon},
  booktitle = {ICCV},
  year      = {2019},
}

@inproceedings{Zhang2017a,
  author    = {Zhang, Hongyi and Cisse, Moustapha and Dauphin, Yann N and Lopez-Paz, David},
  title     = {mixup: Beyond empirical risk minimization},
  booktitle = {ICLR},
  year      = {2018},
}
@inproceedings{yang2020patchattack,
  title={Patchattack: A black-box texture-based attack with reinforcement learning},
  author={Yang, Chenglin and Kortylewski, Adam and Xie, Cihang and Cao, Yinzhi and Yuille, Alan},
  booktitle={ECCV},
  year={2020},
}

@inproceedings{andriushchenko2020square,
  title={Square attack: a query-efficient black-box adversarial attack via random search},
  author={Andriushchenko, Maksym and Croce, Francesco and Flammarion, Nicolas and Hein, Matthias},
  booktitle={ECCV},
  year={2020},
}

@inproceedings{croce2020minimally,
  title={Minimally distorted adversarial examples with a fast adaptive boundary attack},
  author={Croce, Francesco and Hein, Matthias},
  booktitle={ICML},
  year={2020},
}

@inproceedings{croce2020reliable,
  title={Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks},
  author={Croce, Francesco and Hein, Matthias},
  booktitle={ICML},
  year={2020},
}
@article{Lemley2017,
  author    = {Lemley, Joseph and Bazrafkan, Shabab and Corcoran, Peter},
  title     = {Smart augmentation learning an optimal data augmentation strategy},
  journal   = {IEEE Access},
  year      = {2017},
  publisher = {IEEE},
}

@inproceedings{Cubuk2018,
  author    = {Cubuk, Ekin D and Zoph, Barret and Mane, Dandelion and Vasudevan, Vijay and Le, Quoc V},
  title     = {Autoaugment: Learning augmentation policies from data},
  booktitle = {CVPR},
  year      = {2019},
}



@article{Zoph2019,
  author    = {Zoph, Barret and Cubuk, Ekin D and Ghiasi, Golnaz and Lin, Tsung-Yi and Shlens, Jonathon and Le, Quoc V},
  title     = {Learning Data Augmentation Strategies for Object Detection},
  journal   = {arXiv preprint arXiv:1906.11172},
  year      = {2019},
}



@article{Lim2019,
  author    = {Lim, Sungbin and Kim, Ildoo and Kim, Taesup and Kim, Chiheon and Kim, Sungwoong},
  title     = {Fast autoaugment},
  journal   = {arXiv preprint arXiv:1905.00397},
  year      = {2019},
}

@inproceedings{Xie2019a,
  author    = {Xie, Qizhe and Hovy, Eduard and Luong, Minh-Thang and Le, Quoc},
  title     = {Self-training with Noisy Student improves ImageNet classification},
  booktitle = CVPR,
  year      = {2020},
}


@inproceedings{Pang2018,
  author    = {Pang, Tianyu and Du, Chao and Dong, Yinpeng and Zhu, Jun},
  title     = {Towards Robust Detection of Adversarial Examples},
  booktitle = NeurIPS,
  year      = {2018},
}

@inproceedings{Huang2017a,
  author    = {Huang, Xun and Belongie, Serge},
  title     = {Arbitrary style transfer in real-time with adaptive instance normalization},
  booktitle = ICCV,
  year      = {2017},
}

@inproceedings{howard2019searching,
  title     = {Searching for mobilenetv3},
  author    = {Howard, Andrew and Sandler, Mark and Chu, Grace and Chen, Liang-Chieh and Chen, Bo and Tan, Mingxing and Wang, Weijun and Zhu, Yukun and Pang, Ruoming and Vasudevan, Vijay and others},
  booktitle = ICCV,
  year={2019},
}

@article{lopes2019improving,
  title     = {Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation},
  author    = {Lopes, Raphael Gontijo and Yin, Dong and Poole, Ben and Gilmer, Justin and Cubuk, Ekin D},
  journal   = {arXiv preprint arXiv:1906.02611},
  year      = {2019},
}

@inproceedings{carmon2019unlabeled,
  title     = {Unlabeled data improves adversarial robustness},
  author    = {Carmon, Yair and Raghunathan, Aditi and Schmidt, Ludwig and Duchi, John C and Liang, Percy S},
  booktitle = {NeurIPS},
  year      = {2019},
}

@inproceedings{alayrac2019labels,
  title     = {Are Labels Required for Improving Adversarial Robustness?},
  author    = {Uesato, Jonathan and Alayrac, Jean-Baptiste and Huang, Po-Sen and Fawzi, Alhussein and Stanforth, Robert and Kohli, Pushmeet},
  booktitle = {NeurIPS},
  year      = {2019},
}

@inproceedings{schmidt2018adversarially,
  title     = {Adversarially robust generalization requires more data},
  author    = {Schmidt, Ludwig and Santurkar, Shibani and Tsipras, Dimitris and Talwar, Kunal and Madry, Aleksander},
  booktitle = {NeurIPS},
  year.     = {2018},
}

@inproceedings{andriushchenko2020understanding,
  title     = {Understanding and Improving Fast Adversarial Training},
  author    = {Andriushchenko, Maksym and Flammarion, Nicolas},
  booktitle = {NeurIPS},
  year      = {2020},
}

@inproceedings{najafi2019robustness,
  title     = {Robustness to adversarial perturbations in learning from incomplete data},
  author    = {Najafi, Amir and Maeda, Shin-ichi and Koyama, Masanori and Miyato, Takeru},
  booktitle = {NeurIPS},
  year      = {2019},
}

@article{raghunathan2020understanding,
  title     = {Understanding and Mitigating the Tradeoff Between Robustness and Accuracy},
  author    = {Raghunathan, Aditi and Xie, Sang Michael and Yang, Fanny and Duchi, John and Liang, Percy},
  journal   = {arXiv preprint arXiv:2002.10716},
  year      = {2020},
}

@article{raghunathan2019adversarial,
  title     = {Adversarial training can hurt generalization},
  author    = {Raghunathan, Aditi and Xie, Sang Michael and Yang, Fanny and Duchi, John C and Liang, Percy},
  journal   = {arXiv preprint arXiv:1906.06032},
  year      = {2019},
}

@article{li2019inductive,
  title     = {Inductive Bias of Gradient Descent based Adversarial Training on Separable Data},
  author    = {Li, Yan and Fang, Ethan X and Xu, Huan and Zhao, Tuo},
  journal   = {arXiv preprint arXiv:1906.02931},
  year      = {2019},
}

@inproceedings{stutz2019disentangling,
  title     = {Disentangling adversarial robustness and generalization},
  author    = {Stutz, David and Hein, Matthias and Schiele, Bernt},
  booktitle = CVPR,
  year      = {2019},
}

@article{min2020curious,
  title     = {The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization},
  author    = {Min, Yifei and Chen, Lin and Karbasi, Amin},
  journal   = {arXiv preprint arXiv:2002.11080},
  year      = {2020},
}

@article{zhai2019adversarially,
  title     = {Adversarially robust generalization just requires more unlabeled data},
  author    = {Zhai, Runtian and Cai, Tianle and He, Di and Dan, Chen and He, Kun and Hopcroft, John and Wang, Liwei},
  journal   = {arXiv preprint arXiv:1906.00555},
  year      = {2019},
}

@article{nakkiran2019adversarial,
  title     = {Adversarial robustness may be at odds with simplicity},
  author    = {Nakkiran, Preetum},
  journal   = {arXiv preprint arXiv:1901.00532},
  year      = {2019},
}

@article{stutz2019confidence,
  title    = {Confidence-Calibrated Adversarial Training and Detection: More Robust Models Generalizing Beyond the Attack Used During Training},
  author   = {Stutz, David and Hein, Matthias and Schiele, Bernt},
  journal  = {arXiv preprint arXiv:1910.06259},
  year     = {2019},
}

@article{cheng2020cat,
  title    = {CAT: Customized Adversarial Training for Improved Robustness},
  author   = {Cheng, Minhao and Lei, Qi and Chen, Pin-Yu and Dhillon, Inderjit and Hsieh, Cho-Jui},
  journal  = {arXiv preprint arXiv:2002.06789},
  year     = {2020},
}

@article{pang2020boosting,
  title    = {Boosting Adversarial Training with Hypersphere Embedding},
  author   = {Pang, Tianyu and Yang, Xiao and Dong, Yinpeng and Xu, Kun and Su, Hang and Zhu, Jun},
  journal  = {arXiv preprint arXiv:2002.08619},
  year     = {2020},
}

@article{jin2020manifold,
  title    = {Manifold Regularization for Adversarial Robustness},
  author   = {Jin, Charles and Rinard, Martin},
  journal  = {arXiv preprint arXiv:2003.04286},
  year     = {2020},
}

@inproceedings{zhang2019adversarial,
  title    = {Adversarial AutoAugment},
  author   = {Zhang, Xinyu and Wang, Qiang and Zhang, Jian and Zhong, Zhao},
  journal  = {ICLR},
  year     = {2020},
}
@Comment{jabref-meta: databaseType:bibtex;}


@inproceedings{xie2020advprop,
  author    = {Cihang Xie and
               Mingxing Tan and
               Boqing Gong and
               Jiang Wang and
               Alan L. Yuille and
               Quoc V. Le},
  title     = {Adversarial Examples Improve Image Recognition},
  booktitle = {CVPR},
  year      = {2020},
}


@inproceedings{ali2019free_adv_train,
  title={Adversarial training for free!},
  author    = {Ali Shafahi and
               Mahyar Najibi and
               Amin Ghiasi and
               Zheng Xu and
               John P. Dickerson and
               Christoph Studer and
               Larry S. Davis and
               Gavin Taylor and
               Tom Goldstein},
  booktitle={NeurIPS},
  year={2019},
}

@inproceedings{kaiming2020momemtum_constrast,
  author    = {Kaiming He and
               Haoqi Fan and
               Yuxin Wu and
               Saining Xie and
               Ross B. Girshick},
  title     = {Momentum Contrast for Unsupervised Visual Representation Learning},
  booktitle = {CVPR},
  year      = {2020},
}


@inproceedings{szegedy2014intriguing,
  author    = {Christian Szegedy and
               Wojciech Zaremba and
               Ilya Sutskever and
               Joan Bruna and
               Dumitru Erhan and
               Ian J. Goodfellow and
               Rob Fergus},
  editor    = {Yoshua Bengio and
               Yann LeCun},
  title     = {Intriguing properties of neural networks},
  booktitle = {ICLR 2014},
  year      = {2014},
}

@inproceedings{guo2018countering,
  author    = {Chuan Guo and
               Mayank Rana and
               Moustapha Ciss{\'{e}} and
               Laurens van der Maaten},
  title     = {Countering Adversarial Images using Input Transformations},
  booktitle = {ICLR 2018},
  publisher = {OpenReview.net},
  year      = {2018},
}

@article{tolstikhin2021mlp,
  title={Mlp-mixer: An all-mlp architecture for vision},
  author={Tolstikhin, Ilya and Houlsby, Neil and Kolesnikov, Alexander and Beyer, Lucas and Zhai, Xiaohua and Unterthiner, Thomas and Yung, Jessica and Keysers, Daniel and Uszkoreit, Jakob and Lucic, Mario and others},
  journal={arXiv preprint arXiv:2105.01601},
  year={2021},
}

@article{liu2021pay,
  title={Pay Attention to MLPs},
  author={Liu, Hanxiao and Dai, Zihang and So, David R and Le, Quoc V},
  journal={arXiv preprint arXiv:2105.08050},
  year={2021},
}

@article{xue2021go,
  title={Go Wider Instead of Deeper},
  author={Xue, Fuzhao and Shi, Ziji and Lou, Yuxuan and Liu, Yong and You, Yang},
  journal={arXiv preprint arXiv:2107.11817},
  year={2021},
}

@inproceedings{touvron2021going,
  title={Going deeper with image transformers},
  author={Touvron, Hugo and Cord, Matthieu and Sablayrolles, Alexandre and Synnaeve, Gabriel and J{\'e}gou, Herv{\'e}},
  booktitle={ICCV},
  year={2021},
}

@inproceedings{radosavovic2020designing,
  title={Designing network design spaces},
  author={Radosavovic, Ilija and Kosaraju, Raj Prateek and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
  booktitle={CVPR},
  year={2020},
}

@article{chen2021vision,
  title={When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations},
  author={Chen, Xiangning and Hsieh, Cho-Jui and Gong, Boqing},
  journal={arXiv preprint arXiv:2106.01548},
  year={2021},
}

@article{hendrycks2016gaussian,
  title={Gaussian error linear units (gelus)},
  author={Hendrycks, Dan and Gimpel, Kevin},
  journal={arXiv preprint arXiv:1606.08415},
  year={2016},
}

@inproceedings{bello2021revisiting,
  title={Revisiting resnets: Improved training and scaling strategies},
  author={Bello, Irwan and Fedus, William and Du, Xianzhi and Cubuk, Ekin D and Srinivas, Aravind and Lin, Tsung-Yi and Shlens, Jonathon and Zoph, Barret},
  booktitle={NeurIPS},
  year={2021},
}

@inproceedings{Cubuk2019,
  author    = {Cubuk, Ekin D and Zoph, Barret and Shlens, Jonathon and Le, Quoc V},
  title     = {RandAugment: Practical data augmentation with no separate search},
  booktitle = {NeurIPS},
  year      = {2020},
}

@inproceedings{dai2019transformer,
  title={Transformer-xl: Attentive language models beyond a fixed-length context},
  author={Dai, Zihang and Yang, Zhilin and Yang, Yiming and Carbonell, Jaime and Le, Quoc V and Salakhutdinov, Ruslan},
  booktitle = {ACL},
  year      = {2019},
}

@inproceedings{devlin2018bert,
  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  booktitle={NAACL-HLT},
  year={2019},
}



@inproceedings{dosovitskiy2020image,
  title={An image is worth 16x16 words: Transformers for image recognition at scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others},
  booktitle = {ICLR},
  year      = {2020},
}

@inproceedings{Hendrycks2018,
  author    = {Hendrycks, Dan and Dietterich, Thomas G},
  title     = {Benchmarking neural network robustness to common corruptions and surface variations},
  booktitle = {ICLR},
  year      = {2018},
}

@inproceedings{hinton2015distilling,
  title={Distilling the knowledge in a neural network},
  author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
  booktitle = {NeurIPS Workshop},
  year      = {2015},
}

@inproceedings{paul2021vision,
  title={Vision transformers are robust learners},
  author={Paul, Sayak and Chen, Pin-Yu},
  booktitle={AAAI},
  year={2022}
}

@article{zhang2021delving,
  title={Delving Deep into the Generalization of Vision Transformers under Distribution Shifts},
  author={Zhang, Chongzhi and Zhang, Mingyuan and Zhang, Shanghang and Jin, Daisheng and Zhou, Qiang and Cai, Zhongang and Zhao, Haiyu and Yi, Shuai and Liu, Xianglong and Liu, Ziwei},
  journal={arXiv preprint arXiv:2106.07617},
  year={2021}
}

@inproceedings{zhu2021long,
  title={Long-short transformer: Efficient transformers for language and vision},
  author={Zhu, Chen and Ping, Wei and Xiao, Chaowei and Shoeybi, Mohammad and Goldstein, Tom and Anandkumar, Anima and Catanzaro, Bryan},
  booktitle={NeurIPS},
  year={2021}
}

@inproceedings{xie2021segformer,
  title={SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers},
  author={Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M and Luo, Ping},
  booktitle={NeurIPS},
  year={2021}
}

@inproceedings{naseer2021intriguing,
  title={Intriguing Properties of Vision Transformers},
  author={Naseer, Muzammal and Ranasinghe, Kanchana and Khan, Salman and Hayat, Munawar and Khan, Fahad Shahbaz and Yang, Ming-Hsuan},
  booktitle={NeurIPS},
  year={2021}
}

@misc{Authors14,
 author = {Authors},
 title = {The frobnicatable foo filter},
 note = {{BMVC14} submission ID 324. Supplied as additional material {\tt bmvc14.pdf}},
 year = 2014
}

@misc{Authors14b,
 author = {Authors},
 title = {Frobnication tutorial},
 note = {Supplied as additional material {\tt tr.pdf}},
 year = 2014
}

@article{Alpher02,
author = {A. Alpher},
title = {Frobnication},
journal = {Journal of Foo},
volume = 12,
number = 1,
pages = {234--778},
year = 2002
}

@article{Alpher03,
author = {A. Alpher and J.~P.~N. Fotheringham-Smythe},
title = {Frobnication revisited},
journal = {Journal of Foo},
volume = 13,
number = 1,
pages = {234--778},
year = 2003
}

@article{Alpher04,
author = {A. Alpher and J.~P.~N. Fotheringham-Smythe and G. Gamow},
title = {Can a machine frobnicate?},
journal = {Journal of Foo},
volume = 14,
number = 1,
pages = {234--778},
year = 2004
}

% Data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@article{imagenetsketch,
  title={Learning robust global representations by penalizing local predictive power},
  author={Wang, Haohan and Ge, Songwei and Lipton, Zachary and Xing, Eric P},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}


% Augmentation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@inproceedings{
mixup,
title={mixup: Beyond Empirical Risk Minimization},
author={Hongyi Zhang and Moustapha Cisse and Yann N. Dauphin and David Lopez-Paz},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=r1Ddp1-Rb},
}

@inproceedings{cutmix,
  title={Cutmix: Regularization strategy to train strong classifiers with localizable features},
  author={Yun, Sangdoo and Han, Dongyoon and Oh, Seong Joon and Chun, Sanghyuk and Choe, Junsuk and Yoo, Youngjoon},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={6023--6032},
  year={2019}
}

@inproceedings{randomerasing,
  title={Random erasing data augmentation},
  author={Zhong, Zhun and Zheng, Liang and Kang, Guoliang and Li, Shaozi and Yang, Yi},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={34},
  number={07},
  pages={13001--13008},
  year={2020}
}

@inproceedings{repeatedaugment,
  title={Augment your batch: Improving generalization through instance repetition},
  author={Hoffer, Elad and Ben-Nun, Tal and Hubara, Itay and Giladi, Niv and Hoefler, Torsten and Soudry, Daniel},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8129--8138},
  year={2020}
}

@inproceedings{randaugment,
 author = {Cubuk, Ekin Dogus and Zoph, Barret and Shlens, Jon and Le, Quoc},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {18613--18624},
 title = {RandAugment: Practical Automated Data Augmentation with a Reduced Search Space},
 volume = {33},
 year = {2020}
}

@article{esteva2021deep,
  title={Deep learning-enabled medical computer vision},
  author={Esteva, Andre and Chou, Katherine and Yeung, Serena and Naik, Nikhil and Madani, Ali and Mottaghi, Ali and Liu, Yun and Topol, Eric and Dean, Jeff and Socher, Richard},
  journal={NPJ digital medicine},
  volume={4},
  number={1},
  pages={5},
  year={2021},
  publisher={Nature Publishing Group UK London}
}
% Backbone
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% CNN
@inproceedings{resnet,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}


@inproceedings{resnext,
  title={Aggregated residual transformations for deep neural networks},
  author={Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1492--1500},
  year={2017}
}

@inproceedings{mobilenetv2,
  title={Mobilenetv2: Inverted residuals and linear bottlenecks},
  author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4510--4520},
  year={2018}
}

@inproceedings{nonlocal,
  title={Non-local neural networks},
  author={Wang, Xiaolong and Girshick, Ross and Gupta, Abhinav and He, Kaiming},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={7794--7803},
  year={2018}
}

@inproceedings{efficientnet,
  title={Efficientnet: Rethinking model scaling for convolutional neural networks},
  author={Tan, Mingxing and Le, Quoc},
  booktitle={International conference on machine learning},
  pages={6105--6114},
  year={2019},
  organization={PMLR}
}

@article{convmixer,
  title={Patches Are All You Need?},
  author={Trockman, Asher and Kolter, J Zico},
  journal={arXiv preprint arXiv:2201.09792},
  year={2022}
}

@Article{convnext,
  author    = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
  title     = {A ConvNet for the 2020s},
  booktitle = {CVPR},
  year      = {2022},
}


@article{abnar2020quantifying,
  title={Quantifying attention flow in transformers},
  author={Abnar, Samira and Zuidema, Willem},
  journal={arXiv preprint arXiv:2005.00928},
  year={2020}
}

@inproceedings{zhou2021ibot,
  title     = {iBOT: Image BERT Pre-Training with Online Tokenizer},
  author    = {Zhou, Jinghao and Wei, Chen and Wang, Huiyu and Shen, Wei and Xie, Cihang and Yuille, Alan and Kong, Tao},
  booktitle = {ICLR},
  year      = {2022}
}


@inproceedings{graham2021levit,
  title={LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference},
  author={Graham, Benjamin and El-Nouby, Alaaeldin and Touvron, Hugo and Stock, Pierre and Joulin, Armand and J{\'e}gou, Herv{\'e} and Douze, Matthijs},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={12259--12269},
  year={2021}
}

@inproceedings{xie2021cotr,
  title={Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation},
  author={Xie, Yutong and Zhang, Jianpeng and Shen, Chunhua and Xia, Yong},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={171--180},
  year={2021},
  organization={Springer}
}

@article{park2021vision,
  title={Vision transformer for covid-19 cxr diagnosis using chest x-ray feature corpus},
  author={Park, Sangjoon and Kim, Gwanghyun and Oh, Yujin and Seo, Joon Beom and Lee, Sang Min and Kim, Jin Hwan and Moon, Sungjun and Lim, Jae-Kwang and Ye, Jong Chul},
  journal={arXiv preprint arXiv:2103.07055},
  year={2021}
}

@inproceedings{zhang2021transfuse,
  title={Transfuse: Fusing transformers and cnns for medical image segmentation},
  author={Zhang, Yundong and Liu, Huiye and Hu, Qiang},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={14--24},
  year={2021},
  organization={Springer}
}

@inproceedings{beit,
title={{BEiT}: {BERT} Pre-Training of Image Transformers},
  author={Bao, Hangbo and Dong, Li and Wei, Furu},
  booktitle = {ICLR},
  year={2022}
}

@article{chen2021transunet,
  title={Transunet: Transformers make strong encoders for medical image segmentation},
  author={Chen, Jieneng and Lu, Yongyi and Yu, Qihang and Luo, Xiangde and Adeli, Ehsan and Wang, Yan and Lu, Le and Yuille, Alan L and Zhou, Yuyin},
  journal={arXiv preprint arXiv:2102.04306},
  year={2021}
}

@article{convstem,
  title={Early convolutions help transformers see better},
  author={Xiao, Tete and Dollar, Piotr and Singh, Mannat and Mintun, Eric and Darrell, Trevor and Girshick, Ross},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}



@inproceedings{bai2020vitsVScnns,
    title     = {Are Transformers More Robust Than CNNs?},
    author    = {Bai, Yutong and Mei, Jieru and Yuille, Alan and Xie, Cihang},
    booktitle = {NeurIPS},
    year      = {2021},
}

@article{raghu2021vision,
  title={Do vision transformers see like convolutional neural networks?},
  author={Raghu, Maithra and Unterthiner, Thomas and Kornblith, Simon and Zhang, Chiyuan and Dosovitskiy, Alexey},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={12116--12128},
  year={2021}
}

@inproceedings{touvron2021training,
  title={Training data-efficient image transformers \& distillation through attention},
  author={Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and J{\'e}gou, Herv{\'e}},
  booktitle={International Conference on Machine Learning},
  pages={10347--10357},
  year={2021},
  organization={PMLR}
}


% Self Supervised Learning
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@inproceedings{caron2021emerging,
  title={Emerging Properties in Self-Supervised Vision Transformers},
  author={Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J\'egou, Herv\'e  and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand},
  booktitle={Proceedings of the International Conference on Computer Vision (ICCV)},
  year={2021}
}

@Article{MaskedAutoencoders2021,
  author  = {Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Doll{\'a}r and Ross Girshick},
  journal = {arXiv:2111.06377},
  title   = {Masked Autoencoders Are Scalable Vision Learners},
  year    = {2021},
}

@inproceedings{he2022masked,
  title={Masked autoencoders are scalable vision learners},
  author={He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao and Doll{\'a}r, Piotr and Girshick, Ross},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16000--16009},
  year={2022}
}


% Robustness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@article{advtransformer,
  title={Are Transformers more robust than CNNs?},
  author={Bai, Yutong and Mei, Jieru and Yuille, Alan L and Xie, Cihang},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

@inproceedings{tan2021efficientnetv2,
  title     = {Efficientnetv2: Smaller models and faster training},
  author    = {Tan, Mingxing and Le, Quoc},
  booktitle = ICML,
  year      = {2021},
}

@article{jumper2021highly,
  title={Highly accurate protein structure prediction with AlphaFold},
  author={Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and {\v{Z}}{\'\i}dek, Augustin and Potapenko, Anna and others},
  journal={Nature},
  volume={596},
  number={7873},
  pages={583--589},
  year={2021},
  publisher={Nature Publishing Group}
}

@article{bai2021transformers,
  title={Are Transformers more robust than CNNs?},
  author={Bai, Yutong and Mei, Jieru and Yuille, Alan L and Xie, Cihang},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={26831--26843},
  year={2021}
}

@article{xie2021unified,
  title={Unified 2D and 3D Pre-training for Medical Image classification and Segmentation},
  author={Xie, Yutong and Zhang, Jianpeng and Xia, Yong and Wu, Qi},
  journal={arXiv preprint arXiv:2112.09356},
  year={2021}
}

@inproceedings{ma2022benchmarking,
  title={Benchmarking and Boosting Transformers for Medical Image Classification},
  author={Ma, DongAo and Hosseinzadeh Taher, Mohammad Reza and Pang, Jiaxuan and Islam, Nahid UI and Haghighi, Fatemeh and Gotway, Michael B and Liang, Jianming},
  booktitle={MICCAI Workshop on Domain Adaptation and Representation Transfer},
  pages={12--22},
  year={2022},
  organization={Springer}
}

@article{taher2022caid,
  title={Caid: Context-aware instance discrimination for self-supervised learning in medical imaging},
  author={Taher, Mohammad Reza Hosseinzadeh and Haghighi, Fatemeh and Gotway, Michael B and Liang, Jianming},
  journal={arXiv preprint arXiv:2204.07344},
  year={2022}
}

@inproceedings{guo2022discriminative,
  title={Discriminative, Restorative, and Adversarial Learning: Stepwise Incremental Pretraining},
  author={Guo, Zuwei and Islam, Nahid UI and Gotway, Michael B and Liang, Jianming},
  booktitle={MICCAI Workshop on Domain Adaptation and Representation Transfer},
  pages={66--76},
  year={2022},
  organization={Springer}
}

@article{han2022survey,
  title={A survey on vision transformer},
  author={Han, Kai and Wang, Yunhe and Chen, Hanting and Chen, Xinghao and Guo, Jianyuan and Liu, Zhenhua and Tang, Yehui and Xiao, An and Xu, Chunjing and Xu, Yixing and others},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2022},
  publisher={IEEE}
}

@article{khan2021transformers,
  title={Transformers in vision: A survey},
  author={Khan, Salman and Naseer, Muzammal and Hayat, Munawar and Zamir, Syed Waqas and Khan, Fahad Shahbaz and Shah, Mubarak},
  journal={ACM Computing Surveys (CSUR)},
  year={2021},
  publisher={ACM New York, NY}
}

@article{brown2020language,
  title={Language models are few-shot learners},
  author={Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={1877--1901},
  year={2020}
}

% Optimzer
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@inproceedings{
adamw,
title={Decoupled Weight Decay Regularization},
author={Ilya Loshchilov and Frank Hutter},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=Bkg6RiCqY7},
}

@inproceedings{brock2021high,
  title     = {High-performance large-scale image recognition without normalization},
  author    = {Brock, Andy and De, Soham and Smith, Samuel L and Simonyan, Karen},
  booktitle = {ICML},
  year      = {2021},
}

@article{joshi2020spanbert,
  title={Spanbert: Improving pre-training by representing and predicting spans},
  author={Joshi, Mandar and Chen, Danqi and Liu, Yinhan and Weld, Daniel S and Zettlemoyer, Luke and Levy, Omer},
  journal={Transactions of the Association for Computational Linguistics},
  volume={8},
  pages={64--77},
  year={2020},
  publisher={MIT Press}
}

@inproceedings{liu2022convnet,
  title={A convnet for the 2020s},
  author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11976--11986},
  year={2022}
}

@article{hinton2006reducing,
  title={Reducing the dimensionality of data with neural networks},
  author={Hinton, Geoffrey E and Salakhutdinov, Ruslan R},
  journal={science},
  volume={313},
  number={5786},
  pages={504--507},
  year={2006},
  publisher={American Association for the Advancement of Science}
}

@article{steiner2021train,
  title={How to train your vit? data, augmentation, and regularization in vision transformers},
  author={Steiner, Andreas and Kolesnikov, Alexander and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas},
  journal={arXiv preprint arXiv:2106.10270},
  year={2021}
}

@article{bommasani2021opportunities,
  title={On the opportunities and risks of foundation models},
  author={Bommasani, Rishi and Hudson, Drew A and Adeli, Ehsan and Altman, Russ and Arora, Simran and von Arx, Sydney and Bernstein, Michael S and Bohg, Jeannette and Bosselut, Antoine and Brunskill, Emma and others},
  journal={arXiv preprint arXiv:2108.07258},
  year={2021}
}

@article{wightman2021resnet,
  title={Resnet strikes back: An improved training procedure in timm},
  author={Wightman, Ross and Touvron, Hugo and J{\'e}gou, Herv{\'e}},
  journal={arXiv preprint arXiv:2110.00476},
  year={2021}
}


@article{sun2019ernie,
  title={Ernie: Enhanced representation through knowledge integration},
  author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Chen, Xuyi and Zhang, Han and Tian, Xin and Zhu, Danxiang and Tian, Hao and Wu, Hua},
  journal={arXiv preprint arXiv:1904.09223},
  year={2019}
}

@inproceedings{sadafi2019multiclass,
  title={Multiclass deep active learning for detecting red blood cell subtypes in brightfield microscopy},
  author={Sadafi, Ario and Koehler, Niklas and Makhro, Asya and Bogdanova, Anna and Navab, Nassir and Marr, Carsten and Peng, Tingying},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={685--693},
  year={2019},
  organization={Springer}
}


@inproceedings{huang2021semi,
  title={Semi-supervised active learning with temporal output discrepancy},
  author={Huang, Siyu and Wang, Tianyang and Xiong, Haoyi and Huan, Jun and Dou, Dejing},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={3447--3456},
  year={2021}
}

@article{sajjadi2016regularization,
  title={Regularization with stochastic transformations and perturbations for deep semi-supervised learning},
  author={Sajjadi, Mehdi and Javanmardi, Mehran and Tasdizen, Tolga},
  journal={Advances in neural information processing systems},
  volume={29},
  pages={1163--1171},
  year={2016}
}

@article{birodkar2019semantic,
  title={Semantic Redundancies in Image-Classification Datasets: The 10\% You Don't Need},
  author={Birodkar, Vighnesh and Mobahi, Hossein and Bengio, Samy},
  journal={arXiv preprint arXiv:1901.11409},
  year={2019}
}

@article{chitta2019less,
  title={Less is more: An exploration of data redundancy with active dataset subsampling},
  author={Chitta, Kashyap and Alvarez, Jose M and Haussmann, Elmar and Farabet, Clement},
  journal={arXiv preprint arXiv:1905.12737},
  year={2019}
}

@inproceedings{didaci2012analysis,
  title={Analysis of co-training algorithm with very small training sets},
  author={Didaci, Luca and Fumera, Giorgio and Roli, Fabio},
  booktitle={Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)},
  pages={719--726},
  year={2012},
  organization={Springer}
}

@article{corbiere2019addressing,
  title={Addressing failure prediction by learning model confidence},
  author={Corbi{\`e}re, Charles and Thome, Nicolas and Bar-Hen, Avner and Cord, Matthieu and P{\'e}rez, Patrick},
  journal={arXiv preprint arXiv:1910.04851},
  year={2019}
}

@article{berthelot2019remixmatch,
  title={Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring},
  author={Berthelot, David and Carlini, Nicholas and Cubuk, Ekin D and Kurakin, Alex and Sohn, Kihyuk and Zhang, Han and Raffel, Colin},
  journal={arXiv preprint arXiv:1911.09785},
  year={2019}
}


@article{berthelot2019mixmatch,
  title={Mixmatch: A holistic approach to semi-supervised learning},
  author={Berthelot, David and Carlini, Nicholas and Goodfellow, Ian and Papernot, Nicolas and Oliver, Avital and Raffel, Colin},
  journal={arXiv preprint arXiv:1905.02249},
  year={2019}
}


@article{johnson2019mimic,
  title={MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports},
  author={Johnson, Alistair EW and Pollard, Tom J and Berkowitz, Seth J and Greenbaum, Nathaniel R and Lungren, Matthew P and Deng, Chih-ying and Mark, Roger G and Horng, Steven},
  journal={Scientific data},
  volume={6},
  number={1},
  pages={1--8},
  year={2019},
  publisher={Nature Publishing Group}
}


@article{bao2021beit,
  title={BEiT: BERT Pre-Training of Image Transformers},
  author={Bao, Hangbo and Dong, Li and Wei, Furu},
  journal={arXiv preprint arXiv:2106.08254},
  year={2021}
}


@inproceedings{chen2020dynamic,
  title={Dynamic convolution: Attention over convolution kernels},
  author={Chen, Yinpeng and Dai, Xiyang and Liu, Mengchen and Chen, Dongdong and Yuan, Lu and Liu, Zicheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11030--11039},
  year={2020}
}

@article{zanardo2018abdominal,
  title={Abdominal CT: a radiologist-driven adjustment of the dose of iodinated contrast agent approaches a calculation per lean body weight},
  author={Zanardo, Moreno and Doniselli, Fabio Martino and Esseridou, Anastassia and Tritella, Stefania and Mattiuz, Chiara and Menicagli, Laura and Di Leo, Giovanni and Sardanelli, Francesco},
  journal={European radiology experimental},
  volume={2},
  number={1},
  pages={1--7},
  year={2018},
  publisher={Springer}
}


@article{si2018improved,
  title={Improved peritoneal cavity and abdominal organ imaging using a biphasic contrast agent protocol and spectral photon counting computed tomography K-edge imaging},
  author={Si-Mohamed, Salim and Thivolet, Arnaud and Bonnot, Pierre-Emmanuel and Bar-Ness, Daniel and K{\'e}p{\'e}n{\'e}kian, Vahan and Cormode, David P and Douek, Philippe and Rousset, Pascal},
  journal={Investigative radiology},
  volume={53},
  number={10},
  pages={629},
  year={2018},
  publisher={Wolters Kluwer Health}
}


@article{mitsuzaki1996multiple,
  title={Multiple-phase helical CT of the liver for detecting small hepatomas in patients with liver cirrhosis: contrast-injection protocol and optimal timing.},
  author={Mitsuzaki, Katsuhiko and Yamashita, Yasuyuki and Ogata, Ichiro and Nishiharu, Taiji and Urata, Joji and Takahashi, Mutsumasa},
  journal={AJR. American journal of roentgenology},
  volume={167},
  number={3},
  pages={753--757},
  year={1996},
  publisher={Am Roentgen Ray Soc}
}


@inproceedings{zhu2019multi,
  title={Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma},
  author={Zhu, Zhuotun and Xia, Yingda and Xie, Lingxi and Fishman, Elliot K and Yuille, Alan L},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={3--12},
  year={2019},
  organization={Springer}
}


@article{zhu2020segmentation,
  title={Segmentation for classification of screening pancreatic neuroendocrine tumors},
  author={Zhu, Zhuotun and Lu, Yongyi and Shen, Wei and Fishman, Elliot K and Yuille, Alan L},
  journal={arXiv preprint arXiv:2004.02021},
  year={2020}
}


@inproceedings{choi2018stargan,
  title={Stargan: Unified generative adversarial networks for multi-domain image-to-image translation},
  author={Choi, Yunjey and Choi, Minje and Kim, Munyoung and Ha, Jung-Woo and Kim, Sunghun and Choo, Jaegul},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={8789--8797},
  year={2018}
}


@article{caron2020unsupervised,
  title={Unsupervised learning of visual features by contrasting cluster assignments},
  author={Caron, Mathilde and Misra, Ishan and Mairal, Julien and Goyal, Priya and Bojanowski, Piotr and Joulin, Armand},
  journal={arXiv preprint arXiv:2006.09882},
  year={2020}
}

@article{tsai2021rsna,
  title={The RSNA International COVID-19 Open Radiology Database (RICORD)},
  author={Tsai, Emily B and Simpson, Scott and Lungren, Matthew P and Hershman, Michelle and Roshkovan, Leonid and Colak, Errol and Erickson, Bradley J and Shih, George and Stein, Anouk and Kalpathy-Cramer, Jayashree and others},
  journal={Radiology},
  volume={299},
  number={1},
  pages={E204--E213},
  year={2021},
  publisher={Radiological Society of North America}
}


@inproceedings{zhou2017cyst,
  title={Deep supervision for pancreatic cyst segmentation in abdominal CT scans},
  author={Zhou, Yuyin and Xie, Lingxi and Fishman, Elliot K and Yuille, Alan L},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={222--230},
  year={2017},
  organization={Springer}
}


@article{kim2021selfmatch,
  title={SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning},
  author={Kim, Byoungjip and Choo, Jinho and Kwon, Yeong-Dae and Joe, Seongho and Min, Seungjai and Gwon, Youngjune},
  journal={arXiv preprint arXiv:2101.06480},
  year={2021}
}

@article{masoudi2021quick,
  title={Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research},
  author={Masoudi, Samira and Harmon, Stephanie AA and Mehralivand, Sherif and Walker, Stephanie M and Raviprakash, Harish and Bagci, Ulas and Choyke, Peter L and Turkbey, Baris},
  journal={Journal of Medical Imaging},
  volume={8},
  number={1},
  pages={010901},
  year={2021},
  publisher={International Society for Optics and Photonics}
}


@inproceedings{tzeng2017adversarial,
  title={Adversarial discriminative domain adaptation},
  author={Tzeng, Eric and Hoffman, Judy and Saenko, Kate and Darrell, Trevor},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={7167--7176},
  year={2017}
}


@article{landman2017multiatlas,
  title={Multi-atlas labeling beyond the cranial vault-workshop and challenge},
  author={Landman, Bennett and Xu, Zhoubing and Eugenio Igelsias, Juan and Styner, Martin and Robin Langerak, Thomas and Klein, Arno},
  year={2017}
}


@article{xu2021rethinking,
  title={Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective},
  author={Xu, Jiarui and Wang, Xiaolong},
  journal={arXiv preprint arXiv:2103.17263},
  year={2021}
}

@article{heller2019kits19,
  title={The kits19 challenge data: 300 kidney tumor cases with clinical context, ct semantic segmentations, and surgical outcomes},
  author={Heller, Nicholas and Sathianathen, Niranjan and Kalapara, Arveen and Walczak, Edward and Moore, Keenan and Kaluzniak, Heather and Rosenberg, Joel and Blake, Paul and Rengel, Zachary and Oestreich, Makinna and others},
  journal={arXiv preprint arXiv:1904.00445},
  year={2019}
}


@article{masoudi2018new,
  title={A new dataset of computed-tomography angiography images for computer-aided detection of pulmonary embolism},
  author={Masoudi, Mojtaba and Pourreza, Hamid-Reza and Saadatmand-Tarzjan, Mahdi and Eftekhari, Noushin and Zargar, Fateme Shafiee and Rad, Masoud Pezeshki},
  journal={Scientific data},
  volume={5},
  number={1},
  pages={1--9},
  year={2018},
  publisher={Nature Publishing Group}
}


@article{krizhevsky2009learning,
  title={Learning multiple layers of features from tiny images},
  author={Krizhevsky, Alex and Hinton, Geoffrey and others},
  year={2009},
  publisher={Citeseer}
}

@article{netzer2011reading,
  title={Reading digits in natural images with unsupervised feature learning},
  author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y},
  year={2011}
}


@article{lecun1998gradient,
  title={Gradient-based learning applied to document recognition},
  author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
  journal={Proceedings of the IEEE},
  volume={86},
  number={11},
  pages={2278--2324},
  year={1998},
  publisher={Ieee}
}


@inproceedings{cordts2016cityscapes,
  title={The cityscapes dataset for semantic urban scene understanding},
  author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={3213--3223},
  year={2016}
}


@article{cherti2021effect,
  title={Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images},
  author={Cherti, Mehdi and Jitsev, Jenia},
  journal={arXiv preprint arXiv:2106.00116},
  year={2021}
}


@inproceedings{reiss2021every,
  title={Every annotation counts: Multi-label deep supervision for medical image segmentation},
  author={Rei{\ss}, Simon and Seibold, Constantin and Freytag, Alexander and Rodner, Erik and Stiefelhagen, Rainer},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9532--9542},
  year={2021}
}


@article{shih2019augmenting,
  title={Augmenting the National Institutes of Health chest radiograph dataset with expert annotations of possible pneumonia},
  author={Shih, George and Wu, Carol C and Halabi, Safwan S and Kohli, Marc D and Prevedello, Luciano M and Cook, Tessa S and Sharma, Arjun and Amorosa, Judith K and Arteaga, Veronica and Galperin-Aizenberg, Maya and others},
  journal={Radiology: Artificial Intelligence},
  volume={1},
  number={1},
  pages={e180041},
  year={2019},
  publisher={Radiological Society of North America}
}


@article{yang2021radimagenet,
  title={RadImageNet: A Large-scale Radiologic Dataset for Enhancing Deep Learning Transfer Learning Research},
  author={Yang, Yang and Mei, Xueyan and Robson, Philip and Marinelli, Brett and Huang, Mingqian and Doshi, Amish and Jacobi, Adam and Link, Katherine and Yang, Thomas and Cao, Chendi and others},
  year={2021}
}


@article{bustos2020padchest,
  title={Padchest: A large chest x-ray image dataset with multi-label annotated reports},
  author={Bustos, Aurelia and Pertusa, Antonio and Salinas, Jose-Maria and de la Iglesia-Vay{\'a}, Maria},
  journal={Medical image analysis},
  volume={66},
  pages={101797},
  year={2020},
  publisher={Elsevier}
}


@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 Ayed, Ismail Ben},
  booktitle={Medical Imaging with Deep Learning},
  pages={365--381},
  year={2020},
  organization={PMLR}
}


@article{wang2020iterative,
  title={Iterative label denoising network: Segmenting male pelvic organs in CT from 3D bounding box annotations},
  author={Wang, Shuai and Wang, Qian and Shao, Yeqin and Qu, Liangqiong and Lian, Chunfeng and Lian, Jun and Shen, Dinggang},
  journal={IEEE Transactions on Biomedical Engineering},
  volume={67},
  number={10},
  pages={2710--2720},
  year={2020},
  publisher={IEEE}
}


@inproceedings{zhang2020interactive,
  title={Interactive object segmentation with inside-outside guidance},
  author={Zhang, Shiyin and Liew, Jun Hao and Wei, Yunchao and Wei, Shikui and Zhao, Yao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12234--12244},
  year={2020}
}


@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={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={3159--3167},
  year={2016}
}


@inproceedings{lee2020scribble2label,
  title={Scribble2label: Scribble-supervised cell segmentation via self-generating pseudo-labels with consistency},
  author={Lee, Hyeonsoo and Jeong, Won-Ki},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={14--23},
  year={2020},
  organization={Springer}
}


@article{gregory2021semantic,
  title={Semantic Instance Segmentation of Kidney Cysts in MR Images: A Fully Automated 3D Approach Developed Through Active Learning},
  author={Gregory, Adriana V and Anaam, Deema A and Vercnocke, Andrew J and Edwards, Marie E and Torres, Vicente E and Harris, Peter C and Erickson, Bradley J and Kline, Timothy L},
  journal={Journal of Digital Imaging},
  pages={1--15},
  year={2021},
  publisher={Springer}
}


@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={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={175--183},
  year={2019},
  organization={Springer}
}


@article{zhao2021dsal,
  title={Dsal: Deeply supervised active learning from strong and weak labelers for biomedical image segmentation},
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AUTHOR = {Syer, Tom and Mehta, Pritesh and Antonelli, Michela and Mallett, Sue and Atkinson, David and Ourselin, Sébastien and Punwani, Shonit},
TITLE = {Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies},
JOURNAL = {Cancers},
VOLUME = {13},
YEAR = {2021},
NUMBER = {13},
ARTICLE-NUMBER = {3318},
}

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% The bibs below are copied from my dissertation

@article{yushkevich2006user,
  title={User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability},
  author={Yushkevich, Paul A and Piven, Joseph and Hazlett, Heather Cody and Smith, Rachel Gimpel and Ho, Sean and Gee, James C and Gerig, Guido},
  journal={Neuroimage},
  volume={31},
  number={3},
  pages={1116--1128},
  year={2006},
  publisher={Elsevier}
}

@article{zhou2021active,
  title={Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts},
  author={Zhou, Zongwei and Shin, Jae Y and Gurudu, Suryakanth R and Gotway, Michael B and Liang, Jianming},
  journal={Medical Image Analysis},
  pages={101997},
  year={2021},
  publisher={Elsevier}
}

@article{shi2020review,
  title={Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19},
  author={Shi, Feng and Wang, Jun and Shi, Jun and Wu, Ziyan and Wang, Qian and Tang, Zhenyu and He, Kelei and Shi, Yinghuan and Shen, Dinggang},
  journal={IEEE reviews in biomedical engineering},
  year={2020},
  publisher={IEEE}
}

@article{dong2020role,
  title={The role of imaging in the detection and management of COVID-19: a review},
  author={Dong, Di and Tang, Zhenchao and Wang, Shuo and Hui, Hui and Gong, Lixin and Lu, Yao and Xue, Zhong and Liao, Hongen and Chen, Fang and Yang, Fan and others},
  journal={IEEE reviews in biomedical engineering},
  year={2020},
  publisher={IEEE}
}

@article{mckinney2020international,
  title={International evaluation of an AI system for breast cancer screening},
  author={McKinney, Scott Mayer and Sieniek, Marcin and Godbole, Varun and Godwin, Jonathan and Antropova, Natasha and Ashrafian, Hutan and Back, Trevor and Chesus, Mary and Corrado, Greg S and Darzi, Ara and others},
  journal={Nature},
  volume={577},
  number={7788},
  pages={89--94},
  year={2020},
  publisher={Nature Publishing Group}
}

@inproceedings{alex2017generative,
  title={Generative adversarial networks for brain lesion detection},
  author={Alex, Varghese and KP, Mohammed Safwan and Chennamsetty, Sai Saketh and Krishnamurthi, Ganapathy},
  booktitle={Medical Imaging 2017: Image Processing},
  volume={10133},
  pages={101330G},
  year={2017},
  organization={International Society for Optics and Photonics}
}

@article{schlegl2019f,
  title={f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks},
  author={Schlegl, Thomas and Seeb{\"o}ck, Philipp and Waldstein, Sebastian M and Langs, Georg and Schmidt-Erfurth, Ursula},
  journal={Medical Image Analysis},
  year={2019},
  publisher={Elsevier}
}

@article{lecun2015deep,
  title={Deep learning},
  author={LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
  journal={nature},
  volume={521},
  number={7553},
  pages={436},
  year={2015},
  publisher={Nature Publishing Group}
}

@article{kovashka2016crowdsourcing,
  title={Crowdsourcing in computer vision},
  author={Kovashka, Adriana and Russakovsky, Olga and Fei-Fei, Li and Grauman, Kristen},
  journal={arXiv preprint arXiv:1611.02145},
  year={2016}
}

@article{kuznetsova2020open,
  title={The open images dataset v4},
  author={Kuznetsova, Alina and Rom, Hassan and Alldrin, Neil and Uijlings, Jasper and Krasin, Ivan and Pont-Tuset, Jordi and Kamali, Shahab and Popov, Stefan and Malloci, Matteo and Kolesnikov, Alexander and others},
  journal={International Journal of Computer Vision},
  pages={1--26},
  year={2020},
  publisher={Springer}
}

@article{goodfellow2014generative,
  title={Generative adversarial nets},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  journal={Advances in neural information processing systems},
  volume={27},
  year={2014}
}


@article{goodfellow2016deep,
  title={Deep learning},
  author={Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron and Bengio, Yoshua},
  volume={1},
  number={2},
  year={2016},
  publisher={MIT press Cambridge}
}

@article{beutel2000handbook,
  title={Handbook of Medical Imaging: Medical image processing and analysis},
  author={Beutel, Jacob and Sonka, Milan and Kundel, Harold L and Van Metter, Richard L and Fitzpatrick, J Michael},
  volume={2},
  year={2000},
  publisher={Spie Press}
}

@article{haenssle2018man,
  title={Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists},
  author={Haenssle, Holger A and Fink, Christine and Schneiderbauer, R and Toberer, Ferdinand and Buhl, Timo and Blum, A and Kalloo, A and Hassen, A Ben Hadj and Thomas, Luc and Enk, A and others},
  journal={Annals of Oncology},
  volume={29},
  number={8},
  pages={1836--1842},
  year={2018},
  publisher={Oxford University Press}
}

@article{kooi2017large,
  title={Large scale deep learning for computer aided detection of mammographic lesions},
  author={Kooi, Thijs and Litjens, Geert and Van Ginneken, Bram and Gubern-M{\'e}rida, Albert and S{\'a}nchez, Clara I and Mann, Ritse and den Heeten, Ard and Karssemeijer, Nico},
  journal={Medical image analysis},
  volume={35},
  pages={303--312},
  year={2017},
  publisher={Elsevier}
}

@article{cicero2017training,
  title={Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs},
  author={Cicero, Mark and Bilbily, Alexander and Colak, Errol and Dowdell, Tim and Gray, Bruce and Perampaladas, Kuhan and Barfett, Joseph},
  journal={Investigative radiology},
  volume={52},
  number={5},
  pages={281--287},
  year={2017},
  publisher={LWW}
}

@article{de2018clinically,
  title={Clinically applicable deep learning for diagnosis and referral in retinal disease},
  author={De Fauw, Jeffrey and Ledsam, Joseph R and Romera-Paredes, Bernardino and Nikolov, Stanislav and Tomasev, Nenad and Blackwell, Sam and Askham, Harry and Glorot, Xavier and O’Donoghue, Brendan and Visentin, Daniel and others},
  journal={Nature medicine},
  volume={24},
  number={9},
  pages={1342--1350},
  year={2018},
  publisher={Nature Publishing Group}
}

@article{poplin2018prediction,
  title={Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning},
  author={Poplin, Ryan and Varadarajan, Avinash V and Blumer, Katy and Liu, Yun and McConnell, Michael V and Corrado, Greg S and Peng, Lily and Webster, Dale R},
  journal={Nature Biomedical Engineering},
  volume={2},
  number={3},
  pages={158},
  year={2018},
  publisher={Nature Publishing Group}
}

@article{gulshan2016development,
  title={Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs},
  author={Gulshan, Varun and Peng, Lily and Coram, Marc and Stumpe, Martin C and Wu, Derek and Narayanaswamy, Arunachalam and Venugopalan, Subhashini and Widner, Kasumi and Madams, Tom and Cuadros, Jorge and others},
  journal={Jama},
  volume={316},
  number={22},
  pages={2402--2410},
  year={2016},
  publisher={American Medical Association}
}

@article{yamamoto2019automated,
  title={Automated acquisition of explainable knowledge from unannotated histopathology images},
  author={Yamamoto, Yoichiro and Tsuzuki, Toyonori and Akatsuka, Jun and Ueki, Masao and Morikawa, Hiromu and Numata, Yasushi and Takahara, Taishi and Tsuyuki, Takuji and Tsutsumi, Kotaro and Nakazawa, Ryuto and others},
  journal={Nature communications},
  volume={10},
  number={1},
  pages={1--9},
  year={2019},
  publisher={Nature Publishing Group}
}

@manual{tajbakhsh2020annotation,
  title  = {IEEE Transactions on Medical Imaging Special Issue on Annotation-Efficient Deep Learning for Medical Imaging},
  author = {Tajbakhsh, Nima and Liang, Jianming and Roth, Holger and Terzopoulos, Demetri},
year   = {2020}
}

@article{beck2011systematic,
  title={Systematic analysis of breast cancer morphology uncovers stromal features associated with survival},
  author={Beck, Andrew H and Sangoi, Ankur R and Leung, Samuel and Marinelli, Robert J and Nielsen, Torsten O and Van De Vijver, Marc J and West, Robert B and Van De Rijn, Matt and Koller, Daphne},
  journal={Science translational medicine},
  volume={3},
  number={108},
  pages={108ra113--108ra113},
  year={2011},
  publisher={American Association for the Advancement of Science}
}

@article{charoentong2017pan,
  title={Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade},
  author={Charoentong, Pornpimol and Finotello, Francesca and Angelova, Mihaela and Mayer, Clemens and Efremova, Mirjana and Rieder, Dietmar and Hackl, Hubert and Trajanoski, Zlatko},
  journal={Cell reports},
  volume={18},
  number={1},
  pages={248--262},
  year={2017},
  publisher={Elsevier}
}

@inproceedings{cirecsan2013mitosis,
  title={Mitosis detection in breast cancer histology images with deep neural networks},
  author={Cire{\c{s}}an, Dan C and Giusti, Alessandro and Gambardella, Luca M and Schmidhuber, J{\"u}rgen},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={411--418},
  year={2013},
  organization={Springer}
}

@article{cheng2016computer,
  title={Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans},
  author={Cheng, Jie-Zhi and Ni, Dong and Chou, Yi-Hong and Qin, Jing and Tiu, Chui-Mei and Chang, Yeun-Chung and Huang, Chiun-Sheng and Shen, Dinggang and Chen, Chung-Ming},
  journal={Scientific reports},
  volume={6},
  number={1},
  pages={1--13},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{ardila2019end,
  title={End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography},
  author={Ardila, Diego and Kiraly, Atilla P and Bharadwaj, Sujeeth and Choi, Bokyung and Reicher, Joshua J and Peng, Lily and Tse, Daniel and Etemadi, Mozziyar and Ye, Wenxing and Corrado, Greg and others},
  journal={Nature medicine},
  volume={25},
  number={6},
  pages={954--961},
  year={2019},
  publisher={Nature Publishing Group}
}

@inproceedings{bar2015chest,
  title={Chest pathology detection using deep learning with non-medical training},
  author={Bar, Yaniv and Diamant, Idit and Wolf, Lior and Lieberman, Sivan and Konen, Eli and Greenspan, Hayit},
  booktitle={2015 IEEE 12th international symposium on biomedical imaging (ISBI)},
  pages={294--297},
  year={2015},
  organization={IEEE}
}

@article{esteva2017dermatologist,
  title={Dermatologist-level classification of skin cancer with deep neural networks},
  author={Esteva, Andre and Kuprel, Brett and Novoa, Roberto A and Ko, Justin and Swetter, Susan M and Blau, Helen M and Thrun, Sebastian},
  journal={Nature},
  volume={542},
  number={7639},
  pages={115},
  year={2017},
  publisher={Nature Publishing Group}
}

@inproceedings{deng2009imagenet,
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={248--255},
  year={2009},
  organization={IEEE}
}

@article{zhou2017places,
  title={Places: A 10 million image database for scene recognition},
  author={Zhou, Bolei and Lapedriza, Agata and Khosla, Aditya and Oliva, Aude and Torralba, Antonio},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2017},
  publisher={IEEE}
}

@article{shen2019medical,
  title={Medical Image Computing and Computer Assisted Intervention--MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13--17, 2019, Proceedings},
  author={Shen, Dinggang and Liu, Tianming and Peters, Terry M and Staib, Lawrence H and Essert, Caroline and Zhou, Sean and Yap, Pew-Thian and Khan, Ali},
  volume={11767},
  year={2019},
  publisher={Springer Nature}
}

@article{zhou2019handbook,
  title={Handbook of medical image computing and computer assisted intervention},
  author={Zhou, S Kevin and Rueckert, Daniel and Fichtinger, Gabor},
  year={2019},
  publisher={Academic Press}
}

@article{li2006confidence,
  title={Confidence-based active learning},
  author={Li, Mingkun and Sethi, Ishwar K},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={28},
  number={8},
  pages={1251--1261},
  year={2006},
  publisher={IEEE}
}

@article{settles2009active,
  title={Active learning literature survey},
  author={Settles, Burr},
  year={2009},
  publisher={University of Wisconsin-Madison Department of Computer Sciences}
}

@article{guyon2012active,
  title={Active Learning Challenge: Challenges in Machine Learning, Volumen 6},
  author={Guyon, Isabelle and Cawley, Gavin and Dror, Gideon and Lemaire, Vincent and Statnikov, Alexander},
  year={2012},
  publisher={Microtome publishing}
}

@inproceedings{huang2010active,
  title={Active learning by querying informative and representative examples},
  author={Huang, Sheng-Jun and Jin, Rong and Zhou, Zhi-Hua},
  booktitle={Advances in neural information processing systems},
  pages={892--900},
  year={2010}
}

@inproceedings{liu2017active,
  title={Active learning for human pose estimation},
  author={Liu, Buyu and Ferrari, Vittorio},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={4363--4372},
  year={2017}
}

@inproceedings{konyushkova2017learning,
  title={Learning Active Learning from Data},
  author={Konyushkova, Ksenia and Sznitman, Raphael and Fua, Pascal},
  booktitle={Advances in Neural Information Processing Systems},
  pages={4226--4236},
  year={2017}
}

@inproceedings{mosinska2017active,
  title={Active Learning and Proofreading for Delineation of Curvilinear Structures},
  author={Mosinska, Agata and Tarnawski, Jakub and Fua, Pascal},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={165--173},
  year={2017},
  organization={Springer}
}

@article{zhang2017transfer,
  title={Transfer learning for cross-dataset recognition: a survey},
  author={Zhang, Jing and Li, Wanqing and Ogunbona, Philip},
  journal={arXiv preprint arXiv:1705.04396},
  year={2017}
}

@inproceedings{wang2018deep,
  title={Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation},
  author={Wang, Wenzhe and Lu, Yifei and Wu, Bian and Chen, Tingting and Chen, Danny Z and Wu, Jian},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={723--731},
  year={2018},
  organization={Springer}
}

@inproceedings{huang2018cost,
  title={Cost-effective training of deep cnns with active model adaptation},
  author={Huang, Sheng-Jun and Zhao, Jia-Wei and Liu, Zhao-Yang},
  booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={1580--1588},
  year={2018},
  organization={ACM}
}

@article{lin2018active,
  title={Active self-paced learning for cost-effective and progressive face identification},
  author={Lin, Liang and Wang, Keze and Meng, Deyu and Zuo, Wangmeng and Zhang, Lei},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={40},
  number={1},
  pages={7--19},
  year={2018},
  publisher={IEEE}
}

@inproceedings{chattopadhyay2013joint,
  title={Joint transfer and batch-mode active learning},
  author={Chattopadhyay, Rita and Fan, Wei and Davidson, Ian and Panchanathan, Sethuraman and Ye, Jieping},
  booktitle={International Conference on Machine Learning},
  pages={253--261},
  year={2013}
}

@article{wang2016cost,
  title={Cost-effective active learning for deep image classification},
  author={Wang, Keze and Zhang, Dongyu and Li, Ya and Zhang, Ruimao and Lin, Liang},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  volume={27},
  number={12},
  pages={2591--2600},
  year={2016},
  publisher={IEEE}
}

@inproceedings{beluch2018power,
  title={The power of ensembles for active learning in image classification},
  author={Beluch, William H and Genewein, Tim and N{\"u}rnberger, Andreas and K{\"o}hler, Jan M},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={9368--9377},
  year={2018}
}

@misc{wiggers2020yann,
  title={Yann LeCun and Yoshua Bengio: Self-supervised learning is the key to humanlevel intelligence},
  author={Wiggers, Kyle},
  year={2020},
  publisher={May}
}

@article{lecun1989backpropagation,
  title={Backpropagation applied to handwritten zip code recognition},
  author={LeCun, Yann and Boser, Bernhard and Denker, John S and Henderson, Donnie and Howard, Richard E and Hubbard, Wayne and Jackel, Lawrence D},
  journal={Neural computation},
  volume={1},
  number={4},
  pages={541--551},
  year={1989},
  publisher={MIT Press}
}

@article{bengio2009learning,
  title={Learning deep architectures for AI},
  author={Bengio, Yoshua},
  year={2009},
  publisher={Now Publishers Inc}
}

@article{bi2019active,
  title={An active deep learning approach for minimally supervised PolSAR image classification},
  author={Bi, Haixia and Xu, Feng and Wei, Zhiqiang and Xue, Yong and Xu, Zongben},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={57},
  number={11},
  pages={9378--9395},
  year={2019},
  publisher={IEEE}
}

@inproceedings{gao2020consistency,
  title={Consistency-based semi-supervised active learning: Towards minimizing labeling cost},
  author={Gao, Mingfei and Zhang, Zizhao and Yu, Guo and Ar{\i}k, Sercan {\"O} and Davis, Larry S and Pfister, Tomas},
  booktitle={European Conference on Computer Vision},
  pages={510--526},
  year={2020},
  organization={Springer}
}

@article{zhang2019using,
  title={Using an Ensemble of Incrementally Fine-Tuned CNNs for Cross-Domain Object Category Recognition},
  author={Zhang, Xuesong and Yan, Fei and Zhuang, Yan and Hu, Huosheng and Bu, Chunguang},
  journal={IEEE Access},
  volume={7},
  pages={33822--33833},
  year={2019},
  publisher={IEEE}
}

@article{li2019reverse,
  title={Reverse active learning based atrous DenseNet for pathological image classification},
  author={Li, Yuexiang and Xie, Xinpeng and Shen, Linlin and Liu, Shaoxiong},
  journal={BMC bioinformatics},
  volume={20},
  number={1},
  pages={445},
  year={2019},
  publisher={Springer}
}

@inproceedings{shi2019active,
  title={An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis},
  author={Shi, Xueying and Dou, Qi and Xue, Cheng and Qin, Jing and Chen, Hao and Heng, Pheng-Ann},
  booktitle={International Workshop on Machine Learning in Medical Imaging},
  pages={628--636},
  year={2019},
  organization={Springer}
}

@inproceedings{duan2019improved,
  title={An Improved Active Incremental Fine-Tuning Method Using Outlier Detection Based on the Normal Distribution},
  author={Duan, Guiduo and Wang, Zipeng and Sun, Lin and Ruan, Pei and Lu, Guoming},
  booktitle={2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)},
  pages={888--894},
  year={2019},
  organization={IEEE}
}

@inproceedings{zhu2022assembling,
  title={Assembling and Exploiting Large-scale Existing Labels of Common Thorax Diseases for ImprovedCOVID-19 Classification using Chest Radiographs},
  author={Zhu, Zengle and Kang, Mintong and Yuille, Alan and Zhou, Zongwei},
  booktitle={Radiological Society of North America (RSNA)},
  year={2022}
}

@inproceedings{mao2022towards,
  title={Towards robust vision transformer},
  author={Mao, Xiaofeng and Qi, Gege and Chen, Yuefeng and Li, Xiaodan and Duan, Ranjie and Ye, Shaokai and He, Yuan and Xue, Hui},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12042--12051},
  year={2022}
}

@inproceedings{paul2022vision,
  title={Vision transformers are robust learners},
  author={Paul, Sayak and Chen, Pin-Yu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  pages={2071--2081},
  year={2022}
}

@inproceedings{ding2022scaling,
  title={Scaling up your kernels to 31x31: Revisiting large kernel design in cnns},
  author={Ding, Xiaohan and Zhang, Xiangyu and Han, Jungong and Ding, Guiguang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11963--11975},
  year={2022}
}

@article{touvron2022deit,
  title={Deit iii: Revenge of the vit},
  author={Touvron, Hugo and Cord, Matthieu and J{\'e}gou, Herv{\'e}},
  journal={arXiv preprint arXiv:2204.07118},
  year={2022}
}

@inproceedings{zhou2022understanding,
  title={Understanding the robustness in vision transformers},
  author={Zhou, Daquan and Yu, Zhiding and Xie, Enze and Xiao, Chaowei and Anandkumar, Animashree and Feng, Jiashi and Alvarez, Jose M},
  booktitle={International Conference on Machine Learning},
  pages={27378--27394},
  year={2022},
  organization={PMLR}
}

@inproceedings{zhang2022delving,
  title={Delving deep into the generalization of vision transformers under distribution shifts},
  author={Zhang, Chongzhi and Zhang, Mingyuan and Zhang, Shanghang and Jin, Daisheng and Zhou, Qiang and Cai, Zhongang and Zhao, Haiyu and Liu, Xianglong and Liu, Ziwei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7277--7286},
  year={2022}
}

@inproceedings{yang2022pancreatic,
  title={Pancreatic Ductal Adenocarcinoma (PDAC) Detection Using Per-Slice Annotation},
  author={Yang, Shuojue and Li, Bowen and Liu, Fengze and Chen, Jieneng and Xia, Yingda and Yu, Qihang and Zhu, Zhuotun and Lu, Yongyi and Linda and Kawamoto, Satomi and Park, Seyoun and Fishman, Eliot and Yuille, Alan and Zhou, Zongwei},
  booktitle={Radiological Society of North America (RSNA)},
  year={2022}
}

@inproceedings{xiao2022transforming,
  title={Transforming Radiograph Imaging with Transformers: Comparing Vision Transformers with Convolutional Neural Networks in Multi-Label Thorax Disease Classification},
  author={Xiao, Junfei and Bai, Yutong and Yuille, Alan and Zhou, Zongwei},
  booktitle={Radiological Society of North America (RSNA)},
  year={2022}
}

@inproceedings{chou2022determining,
  title={Determining Effective and Efficient Annotation Strategies to Curate Large-scale Colonoscopy Video Datasets for Polyp Detection},
  author={Chou, Yucheng and Fan, Dengping and Yuille, Alan and Zhou, Zongwei},
  booktitle={Radiological Society of North America (RSNA)},
  year={2022}
}

@inproceedings{xia2022ai,
  title={AI Algorithms Can Assist Radiologists in Early Detection of Pancreatic Neoplasms Through Venous and Arterial CT Imaging},
  author={Xia, Yingda and Yu, Qihang and Chu, Linda and Kawamoto, Satomi and Park, Seyoun and Liu, Fengze and Chen, Jieneng and Zhu, Zhuotun and Li, Bowen and Zhou, Zongwei and Lu, Yongyi and Wang, Yan and Shen, Wei and Xie, Lingxi and Zhou, Yuyin and Fouladi, Daniel and Shayesteh, Shahab and Graves, Scott Jefferson  and Blanco, Alejandra and Zinreich, Eva and Kinzler, Ken and Gruban, Ralph and Vogelstein, Bert and Fishman, Eliot and Yuille, Alan},
  booktitle={Radiological Society of North America (RSNA)},
  year={2022}
}

@inproceedings{xia2022generalizing,
  title={Generalizing AI Algorithms to Abdominal CT Scans Taken from Different Hospitals for Pancreatic Ductal Adenocarcinoma Detection},
  author={Xia, Yingda and Yu, Qihang and Chu, Linda and Kawamoto, Satomi and Park, Seyoun and Liu, Fengze and Chen, Jieneng and Zhu, Zhuotun and Li, Bowen and Zhou, Zongwei and Lu, Yongyi and Wang, Yan and Shen, Wei and Xie, Lingxi and Zhou, Yuyin and Fouladi, Daniel and Shayesteh, Shahab and Graves, Scott Jefferson  and Blanco, Alejandra and Zinreich, Eva and Kinny-Koster, Benedict and Kinzler, Ken and Gruban, Ralph and Vogelstein, Bert and Fishman, Eliot and Yuille, Alan},
  booktitle={Radiological Society of North America (RSNA)},
  year={2022}
}

@inproceedings{xia2022felix,
  title={The Felix Project: Deep Networks To Detect Pancreatic Neoplasms},
  author={Xia, Yingda and Yu, Qihang and Chu, Linda and Kawamoto, Satomi and Park, Seyoun and Liu, Fengze and Chen, Jieneng and Zhu, Zhuotun and Li, Bowen and Zhou, Zongwei and Lu, Yongyi and Wang, Yan and Shen, Wei and Xie, Lingxi and Zhou, Yuyin and Fouladi, Daniel and Shayesteh, Shahab and Graves, Scott Jefferson  and Blanco, Alejandra and Zinreich, Eva and Kinny-Koster, Benedict and Kinzler, Ken and Gruban, Ralph and Vogelstein, Bert and Fishman, Eliot and Yuille, Alan},
  booktitle={In Submission},
  year={2022}
}

@article{budd2019survey,
  title={A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis},
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  publisher={IEEE}
}




@inproceedings{kenton2019bert,
  title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
  author={Kenton, Jacob Devlin Ming-Wei Chang and Toutanova, Lee Kristina},
  booktitle={Proceedings of NAACL-HLT},
  pages={4171--4186},
  year={2019}
}

@Article{Wang2020covid,
	author={Wang, Linda and Lin, Zhong Qiu and Wong, Alexander},
	title={COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images},
	journal={Scientific Reports},
	year={2020},
	month={Nov},
	day={11},
	volume={10},
	number={1},
	pages={19549},
	issn={2045-2322},
	doi={10.1038/s41598-020-76550-z},
	url={https://doi.org/10.1038/s41598-020-76550-z}
}

@misc{node21,
  author       = {Ecem Sogancioglu and
                  Keelin Murphy and
                  Bram van Ginneken},
  title        = {NODE21},
  month        = apr,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v-5},
  doi          = {10.5281/zenodo.5548363},
  url          = {https://doi.org/10.5281/zenodo.5548363}
}



@inproceedings{loshchilov2018decoupled,
  title={Decoupled Weight Decay Regularization},
  author={Loshchilov, Ilya and Hutter, Frank},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

@inproceedings{kinga2015method,
  title={A method for stochastic optimization},
  author={Kinga, D and Adam, J Ba},
  booktitle={International Conference on Learning Representations (ICLR)},
  volume={5},
  year={2015}
}

@inproceedings{loshchilov2016sgdr,
  title={Sgdr: Stochastic gradient descent with warm restarts},
  author={Loshchilov, Ilya and Hutter, Frank},
  booktitle={International Conference on Learning Representations},
  year={2017}
}

@article{you2017large,
  title={Large batch training of convolutional networks},
  author={You, Yang and Gitman, Igor and Ginsburg, Boris},
  journal={arXiv preprint arXiv:1708.03888},
  year={2017}
}

@inproceedings{chen2021empirical,
  title={An empirical study of training self-supervised vision transformers},
  author={Chen, Xinlei and Xie, Saining and He, Kaiming},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={9640--9649},
  year={2021}
}

@inproceedings{zhang2018mixup,
  title={mixup: Beyond Empirical Risk Minimization},
  author={Zhang, Hongyi and Cisse, Moustapha and Dauphin, Yann N and Lopez-Paz, David},
  booktitle={International Conference on Learning Representations},
  year={2018}
}



@inproceedings{huang2016deep,
  title={Deep networks with stochastic depth},
  author={Huang, Gao and Sun, Yu and Liu, Zhuang and Sedra, Daniel and Weinberger, Kilian Q},
  booktitle={European conference on computer vision},
  pages={646--661},
  year={2016},
  organization={Springer}
}

@inproceedings{nguyen2021unsupervised,
  title={Unsupervised region-based anomaly detection in brain mri with adversarial image inpainting},
  author={Nguyen, Bao and Feldman, Adam and Bethapudi, Sarath and Jennings, Andrew and Willcocks, Chris G},
  booktitle={2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  pages={1127--1131},
  year={2021},
  organization={IEEE}
}

@article{sogancioglu2018chest,
  title={Chest X-ray inpainting with deep generative models},
  author={Sogancioglu, Ecem and Hu, Shi and Belli, Davide and van Ginneken, Bram},
  journal={arXiv preprint arXiv:1809.01471},
  year={2018}
}

@inproceedings{armanious2019adversarial,
  title={Adversarial inpainting of medical image modalities},
  author={Armanious, Karim and Mecky, Youssef and Gatidis, Sergios and Yang, Bin},
  booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={3267--3271},
  year={2019},
  organization={IEEE}
}

@inproceedings{cai2022efficientvit,
  title={Efficientvit: Enhanced linear attention for high-resolution low-computation visual recognition},
  author={Cai, Han and Gan, Chuang and Han, Song},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2023}
}

@inproceedings{bai2019self,
  title={Self-supervised learning for cardiac mr image segmentation by anatomical position prediction},
  author={Bai, Wenjia and Chen, Chen and Tarroni, Giacomo and Duan, Jinming and Guitton, Florian and Petersen, Steffen E and Guo, Yike and Matthews, Paul M and Rueckert, Daniel},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={541--549},
  year={2019},
  organization={Springer}
}

@article{clark2020electra,
  title={Electra: Pre-training text encoders as discriminators rather than generators},
  author={Clark, Kevin and Luong, Minh-Thang and Le, Quoc V and Manning, Christopher D},
  journal={arXiv preprint arXiv:2003.10555},
  year={2020}
}



@article{shamshad2022transformers,
  title={Transformers in Medical Imaging: A Survey},
  author={Shamshad, Fahad and Khan, Salman and Zamir, Syed Waqas and Khan, Muhammad Haris and Hayat, Munawar and Khan, Fahad Shahbaz and Fu, Huazhu},
  journal={arXiv preprint arXiv:2201.09873},
  year={2022}
}

@article{matsoukas2021time,
  title={Is it time to replace cnns with transformers for medical images?},
  author={Matsoukas, Christos and Haslum, Johan Fredin and S{\"o}derberg, Magnus and Smith, Kevin},
  journal={arXiv preprint arXiv:2108.09038},
  year={2021}
}



@inproceedings{seyyedkalantari2020chexclusion,
  title={CheXclusion: Fairness gaps in deep chest X-ray classifiers},
  author={Seyyed-Kalantari, Laleh and Liu, Guanxiong and McDermott, Matthew and Chen, Irene Y and Ghassemi, Marzyeh},
  booktitle={BIOCOMPUTING 2021: Proceedings of the Pacific Symposium},
  pages={232--243},
  year={2020},
  organization={World Scientific}
}

@misc{ma2020multilabel,
      title={Multi-label Thoracic Disease Image Classification with Cross-Attention Networks},
      author={Congbo Ma and Hu Wang and Steven C. H. Hoi},
      year={2020},
      eprint={2007.10859},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{hermoza2020region,
  title={Region proposals for saliency map refinement for weakly-supervised disease localisation and classification},
  author={Hermoza, Renato and Maicas, Gabriel and Nascimento, Jacinto C and Carneiro, Gustavo},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={539--549},
  year={2020},
  organization={Springer}
}

@InProceedings{Kim_2021_CVPR,
    author    = {Kim, Eunji and Kim, Siwon and Seo, Minji and Yoon, Sungroh},
    title     = {XProtoNet: Diagnosis in Chest Radiography With Global and Local Explanations},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {15719-15728}
}

@inproceedings{liu2022acpl,
  title={ACPL: Anti-Curriculum Pseudo-Labelling for Semi-Supervised Medical Image Classification},
  author={Liu, Fengbei and Tian, Yu and Chen, Yuanhong and Liu, Yuyuan and Belagiannis, Vasileios and Carneiro, Gustavo},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20697--20706},
  year={2022}
}

@article{allaouzi2019novel,
  title={A novel approach for multi-label chest X-ray classification of common thorax diseases},
  author={Allaouzi, Imane and Ahmed, Mohamed Ben},
  journal={IEEE Access},
  volume={7},
  pages={64279--64288},
  year={2019},
  publisher={IEEE}
}

@misc{pham2020interpreting,
      title={Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels},
      author={Hieu H. Pham and Tung T. Le and Dat T. Ngo and Dat Q. Tran and Ha Q. Nguyen},
      year={2020},
      eprint={2005.12734},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}


@inproceedings{shen2018dynamic,
  title={Dynamic routing on deep neural network for thoracic disease classification and sensitive area localization},
  author={Shen, Yan and Gao, Mingchen},
  booktitle={International Workshop on Machine Learning in Medical Imaging},
  pages={389--397},
  year={2018},
  organization={Springer}
}

@inproceedings{kumar2018boosted,
  title={Boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs},
  author={Kumar, Pulkit and Grewal, Monika and Srivastava, Muktabh Mayank},
  booktitle={International conference image analysis and recognition},
  pages={546--552},
  year={2018},
  organization={Springer}
}

@article{yao2017learning,
  title={Learning to diagnose from scratch by exploiting dependencies among labels},
  author={Yao, Li and Poblenz, Eric and Dagunts, Dmitry and Covington, Ben and Bernard, Devon and Lyman, Kevin},
  journal={arXiv preprint arXiv:1710.10501},
  year={2017}
}

@inproceedings{tao2020revisiting,
  title={Revisiting Rubik’s cube: self-supervised learning with volume-wise transformation for 3D medical image segmentation},
  author={Tao, Xing and Li, Yuexiang and Zhou, Wenhui and Ma, Kai and Zheng, Yefeng},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={238--248},
  year={2020},
  organization={Springer}
}

@inproceedings{carion2020end,
  title={End-to-end object detection with transformers},
  author={Carion, Nicolas and Massa, Francisco and Synnaeve, Gabriel and Usunier, Nicolas and Kirillov, Alexander and Zagoruyko, Sergey},
  booktitle={European conference on computer vision},
  pages={213--229},
  year={2020},
  organization={Springer}
}

@inproceedings{bertasius2021space,
  title={Is space-time attention all you need for video understanding?},
  author={Bertasius, Gedas and Wang, Heng and Torresani, Lorenzo},
  booktitle={ICML},
  year={2021}
}

@article{cordonnier2019relationship,
  title={On the relationship between self-attention and convolutional layers},
  author={Cordonnier, Jean-Baptiste and Loukas, Andreas and Jaggi, Martin},
  journal={arXiv preprint arXiv:1911.03584},
  year={2019}
}

@inproceedings{dAscoli2021ConViTIV,
  title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
  author={St{\'e}phane d'Ascoli and Hugo Touvron and Matthew L. Leavitt and Ari S. Morcos and Giulio Biroli and Levent Sagun},
  booktitle={ICML},
  year={2021}
}


% Publications from Zongwei Zhou
@phdthesis{zhou2021towards,
  title={Towards Annotation-Efficient Deep Learning for Computer-Aided Diagnosis},
  author={Zhou, Zongwei},
  year={2021},
  school={Arizona State University}
}

@inproceedings{xiao2022learning,
  title={Learning from temporal gradient for semi-supervised action recognition},
  author={Xiao, Junfei and Jing, Longlong and Zhang, Lin and He, Ju and She, Qi and Zhou, Zongwei and Yuille, Alan and Li, Yingwei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3252--3262},
  year={2022}
}

@inproceedings{islam2021seeking,
  title={Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection},
  author={Islam, Nahid Ul and Gehlot, Shiv and Zhou, Zongwei and Gotway, Michael B and Liang, Jianming},
  booktitle={International Workshop on Machine Learning in Medical Imaging},
  pages={692--702},
  year={2021},
  organization={Springer}
}

@misc{liang2021systems,
  title={Systems, methods, and apparatuses for implementing a multi-resolution neural network for use with imaging intensive applications including medical imaging},
  author={Liang, Jianming and Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima},
  year={2021},
  month=nov # "~2",
  publisher={Google Patents},
  note={US Patent 11,164,067}
}

@inproceedings{yao2021unsupervised,
  title={Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation},
  author={Yao, Yuan and Liu, Fengze and Zhou, Zongwei and Wang, Yan and Shen, Wei and Yuille, Alan and Lu, Yongyi},
  booktitle={Medical Imaging with Deep Learning},
  year={2021}
}

@article{li2022transforming,
  title={Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives},
  author={Li, Jun and Chen, Junyu and Tang, Yucheng and Landman, Bennett A and Zhou, S Kevin},
  journal={arXiv preprint arXiv:2206.01136},
  year={2022}
}

@misc{siddiquee2021methods,
  title={Methods, systems, and media for discriminating and generating translated images},
  author={Siddiquee, Md Mahfuzur Rahman and Zhou, Zongwei and Feng, Ruibin and Tajbakhsh, Nima and Liang, Jianming},
  year={2021},
  month=nov # "~2",
  publisher={Google Patents},
  note={US Patent 11,164,021}
}

@incollection{zhou2022interpreting,
  title={Interpreting Medical Images},
  author={Zhou, Zongwei and Gotway, Michael and Liang, Jianming},
  booktitle={Intelligent Systems in Medicine and Health: The Role of AI},
  year={2022},
}

@article{chen2022guide,
  title={A Guide to Your First Choice: Addressing Cold Start Problem in Vision Active Learning},
  author={Chen, Liangyu and Bai, Yutong and Huang, Siyu and Lu, Yongyi and Yuille, Alan and Wen, Bihan and Zhou, Zongwei},
  journal={In Submission: Thirty-Sixth Conference on Neural Information Processing Systems},
  year={2022}
}

@article{xiang2021painting,
  title={In-painting Radiography Images for Unsupervised Anomaly Detection},
  author={Xiang, Tiange and Liu, Yongyi and Yuille, Alan L and Zhang, Chaoyi and Cai, Weidong and Zhou, Zongwei},
  journal={arXiv preprint arXiv:2111.13495},
  year={2021}
}

@inproceedings{zhou2017fine,
  title={Fine-tuning convolutional neural networks for biomedical image analysis: actively and incrementally},
  author={Zhou, Zongwei and Shin, Jae and Zhang, Lei and Gurudu, Suryakanth and Gotway, Michael and Liang, Jianming},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={7340--7351},
  year={2017}
}

@article{zhou2021active,
  title={Active, continual fine tuning of convolutional neural networks for reducing annotation efforts},
  author={Zhou, Zongwei and Shin, Jae Y and Gurudu, Suryakanth R and Gotway, Michael B and Liang, Jianming},
  journal={Medical image analysis},
  volume={71},
  pages={101997},
  year={2021},
  publisher={Elsevier}
}

@article{zhou2019integrating,
  title={Integrating active learning and transfer learning for carotid intima-media thickness video interpretation},
  author={Zhou, Zongwei and Shin, Jae and Feng, Ruibin and Hurst, R Todd and Kendall, Christopher B and Liang, Jianming},
  journal={Journal of digital imaging},
  volume={32},
  number={2},
  pages={290--299},
  year={2019},
  publisher={Springer}
}

@inproceedings{ericsson2021well,
  title={How well do self-supervised models transfer?},
  author={Ericsson, Linus and Gouk, Henry and Hospedales, Timothy M},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5414--5423},
  year={2021}
}
@inproceedings{xiao2023delving,
  title={Delving into masked autoencoders for multi-label thorax disease classification},
  author={Xiao, Junfei and Bai, Yutong and Yuille, Alan and Zhou, Zongwei},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={3588--3600},
  year={2023}
}


@article{zhu2020deformable,
  title={Deformable detr: Deformable transformers for end-to-end object detection},
  author={Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng},
  journal={ICLR},
  year={2021}
}


@article{xu2022deep,
  title={Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey},
  author={Xu, Lanyu and Zhu, Simeng and Wen, Ning},
  journal={Physics in Medicine \& Biology},
  volume={67},
  number={22},
  pages={22TR02},
  year={2022},
  publisher={IOP Publishing}
}
@article{zhou2021deep,
  title={Deep reinforcement learning in medical imaging: A literature review},
  author={Zhou, S Kevin and Le, Hoang Ngan and Luu, Khoa and Nguyen, Hien V and Ayache, Nicholas},
  journal={Medical image analysis},
  volume={73},
  pages={102193},
  year={2021},
  publisher={Elsevier}
}
@article{hu2023reinforcement,
  title={Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions},
  author={Hu, Mingzhe and Zhang, Jiahan and Matkovic, Luke and Liu, Tian and Yang, Xiaofeng},
  journal={Journal of Applied Clinical Medical Physics},
  volume={24},
  number={2},
  pages={e13898},
  year={2023},
  publisher={Wiley Online Library}
}
@inproceedings{gao2019distanced,
  title={Distanced LSTM: time-distanced gates in long short-term memory models for lung cancer detection},
  author={Gao, Riqiang and Huo, Yuankai and Bao, Shunxing and Tang, Yucheng and Antic, Sanja L and Epstein, Emily S and Balar, Aneri B and Deppen, Steve and Paulson, Alexis B and Sandler, Kim L and others},
  booktitle={Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 10},
  pages={310--318},
  year={2019},
  organization={Springer}
}

@article{yang2021artificial,
  title={Artificial convolutional neural network in object detection and semantic segmentation for medical imaging analysis},
  author={Yang, Ruixin and Yu, Yingyan},
  journal={Frontiers in oncology},
  volume={11},
  pages={638182},
  year={2021},
  publisher={Frontiers Media SA}
}
@inproceedings{chefer2021transformer,
  title={Transformer interpretability beyond attention visualization},
  author={Chefer, Hila and Gur, Shir and Wolf, Lior},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={782--791},
  year={2021}
}

@inproceedings{zhou2019models,
  title={Models genesis: Generic autodidactic models for 3d medical image analysis},
  author={Zhou, Zongwei and Sodha, Vatsal and Siddiquee, Md Mahfuzur Rahman and Feng, Ruibin and Tajbakhsh, Nima and Gotway, Michael B and Liang, Jianming},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={384--393},
  year={2019},
  organization={Springer}
}

@article{zhou2021models,
  title={Models genesis},
  author={Zhou, Zongwei and Sodha, Vatsal and Pang, Jiaxuan and Gotway, Michael B and Liang, Jianming},
  journal={Medical image analysis},
  volume={67},
  pages={101840},
  year={2021},
  publisher={Elsevier}
}



@inproceedings{zhou2019unet++,
  title={Unet++: Redesigning skip connections to exploit multiscale features in image segmentation},
  author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},
  booktitle={IEEE transactions on medical imaging},
  year={2019},
}

@inproceedings{haghighi2020learning,
  title={Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration},
  author={Haghighi, Fatemeh and Taher, Mohammad Reza Hosseinzadeh and Zhou, Zongwei and Gotway, Michael B and Liang, Jianming},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2020},
  organization={Springer}
}



@inproceedings{feng2020parts2whole,
  title={Parts2Whole: Self-supervised Contrastive Learning via Reconstruction},
  author={Feng, Ruibin and Zhou, Zongwei and Gotway, Michael B and Liang, Jianming},
  booktitle={Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning},
  year={2020},
}

@inproceedings{siddiquee2019learning,
  title={Learning fixed points in generative adversarial networks: From image-to-image translation to disease detection and localization},
  author={Siddiquee, Md Mahfuzur Rahman and Zhou, Zongwei and Tajbakhsh, Nima and Feng, Ruibin and Gotway, Michael B and Bengio, Yoshua and Liang, Jianming},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={191--200},
  year={2019}
}

@article{wang2017comparison,
  title={Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18 F-FDG PET/CT images},
  author={Wang, Hongkai and Zhou, Zongwei and Li, Yingci and Chen, Zhonghua and Lu, Peiou and Wang, Wenzhi and Liu, Wanyu and Yu, Lijuan},
  journal={EJNMMI research},
  volume={7},
  number={1},
  pages={11},
  year={2017},
  publisher={SpringerOpen}
}

@article{kang2021data,
  title={Label-Assemble: Leveraging Multiple Datasets with Partial Labels },
  author={Kang, Mintong and Lu, Yongyi and Yuille, Alan L and Zhou, Zongwei},
  journal={In Submission: Thirty-Sixth Conference on Neural Information Processing Systems},
  url={https://arxiv.org/pdf/2109.12265.pdf},
  year={2021}
}

@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={Proceedings of the 25th international conference on Machine learning},
  pages={1096--1103},
  year={2008}
}

@InProceedings{xie2021simmim,
    author    = {Xie, Zhenda and Zhang, Zheng and Cao, Yue and Lin, Yutong and Bao, Jianmin and Yao, Zhuliang and Dai, Qi and Hu, Han},
    title     = {SimMIM: A Simple Framework for Masked Image Modeling},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {9653-9663}
}

@inproceedings{selvaraju2017grad,
  title={Grad-cam: Visual explanations from deep networks via gradient-based localization},
  author={Selvaraju, Ramprasaath R and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={618--626},
  year={2017}
}
@article{suetens2017fundamentals,
  title={Fundamentals of medical imaging},
  author={Suetens, Paul},
  year={2017},
  publisher={Cambridge university press}
}
@article{liu2022automatic,
  title={Automatic lung segmentation in chest X-ray images using improved U-Net},
  author={Liu, Wufeng and Luo, Jiaxin and Yang, Yan and Wang, Wenlian and Deng, Junkui and Yu, Liang},
  journal={Scientific Reports},
  volume={12},
  number={1},
  pages={8649},
  year={2022},
  publisher={Nature Publishing Group UK London}
}
@inproceedings{ZhangCDL18,
  author       = {Hongyi Zhang and
                  Moustapha Ciss{\'{e}} and
                  Yann N. Dauphin and
                  David Lopez{-}Paz},
  title        = {mixup: Beyond Empirical Risk Minimization},
  booktitle    = {ICLR},
  year         = {2018},
}
@article{van2001computer,
  title={Computer-aided diagnosis in chest radiography: a survey},
  author={Van Ginneken, Bram and Romeny, BM Ter Haar and Viergever, Max A},
  journal={IEEE Transactions on medical imaging},
  volume={20},
  number={12},
  pages={1228--1241},
  year={2001},
  publisher={IEEE}
}
@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}
@article{muller2019does,
  title={When does label smoothing help?},
  author={M{\"u}ller, Rafael and Kornblith, Simon and Hinton, Geoffrey E},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}
@article{zhou2012mining,
  title={Mining anatomical, physiological and pathological information from medical images},
  author={Zhou, Xiang Sean and Zhan, Yiqiang and Raykar, Vikas C and Hermosillo, Gerardo and Bogoni, Luca and Peng, Zhipang},
  journal={ACM SIGKDD Explorations Newsletter},
  volume={14},
  number={1},
  pages={25--34},
  year={2012},
  publisher={ACM New York, NY, USA}
}
@misc{jacobgilpytorchcam,
  title={PyTorch library for CAM methods},
  author={Jacob Gildenblat and contributors},
  year={2021},
  publisher={GitHub},
  howpublished={\url{https://github.com/jacobgil/pytorch-grad-cam}},
}

@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}
}

@article{manson2019image,
  title={Image noise in radiography and tomography: Causes, effects and reduction techniques},
  author={Manson, EN and Ampoh, V Atuwo and Fiagbedzi, E and Amuasi, JH and Flether, JJ and Schandorf, C},
  journal={Curr. Trends Clin. Med. Imaging},
  volume={2},
  number={5},
  pages={555620},
  year={2019}
}

@article{dhar2023challenges,
  title={Challenges of Deep Learning in Medical Image Analysis—Improving Explainability and Trust},
  author={Dhar, Tribikram and Dey, Nilanjan and Borra, Surekha and Sherratt, R Simon},
  journal={IEEE Transactions on Technology and Society},
  volume={4},
  number={1},
  pages={68--75},
  year={2023},
  publisher={IEEE}
}

@article{chandra2020analysis,
  title={Analysis of quantum noise-reducing filters on chest X-ray images: A review},
  author={Chandra, Tej Bahadur and Verma, Kesari},
  journal={Measurement},
  volume={153},
  pages={107426},
  year={2020},
  publisher={Elsevier}
}

@article{siewerdsen1997empirical,
  title={Empirical and theoretical investigation of the noise performance of indirect detection, active matrix flat-panel imagers (AMFPIs) for diagnostic radiology},
  author={Siewerdsen, JH and Antonuk, LE and El-Mohri, Y and Yorkston, J and Huang, W and Boudry, JM and Cunningham, IA},
  journal={Medical physics},
  volume={24},
  number={1},
  pages={71--89},
  year={1997},
  publisher={Wiley Online Library}
}
@article{siewerdsen1998signal,
  title={Signal, noise power spectrum, and detective quantum efficiency of indirect-detection flat-panel imagers for diagnostic radiology},
  author={Siewerdsen, JH and Antonuk, LE and El-Mohri, Y and Yorkston, J and Huang, W and Cunningham, IA},
  journal={Medical physics},
  volume={25},
  number={5},
  pages={614--628},
  year={1998},
  publisher={Wiley Online Library}
}

@article{sprawls1993physical,
  title={Principles of medical imaging},
  author={Shung, K Kirk and Smith, Michael and Tsui, Benjamin MW},
  year={2012},
  publisher={Academic Press}
}

@article{shung2012principles,
  title={Principles of medical imaging},
  author={Shung, K Kirk and Smith, Michael and Tsui, Benjamin MW},
  year={2012},
  publisher={Academic Press}
}

@article{lee2018poisson,
  title={Poisson--Gaussian noise analysis and estimation for low-dose X-ray images in the NSCT domain},
  author={Lee, Sangyoon and Lee, Min Seok and Kang, Moon Gi},
  journal={Sensors},
  volume={18},
  number={4},
  pages={1019},
  year={2018},
  publisher={MDPI}
}

@article{ding2018statistical,
  title={Statistical image reconstruction using mixed Poisson-Gaussian noise model for X-ray CT},
  author={Ding, Qiaoqiao and Long, Yong and Zhang, Xiaoqun and Fessler, Jeffrey A},
  journal={arXiv preprint arXiv:1801.09533},
  year={2018}
}

%local rademacher


%synthetic images for computer vision
@article{AziziKS0F23-syn-data-imagenet-classification,
  author       = {Shekoofeh Azizi and
                  Simon Kornblith and
                  Chitwan Saharia and
                  Mohammad Norouzi and
                  David J. Fleet},
  title        = {Synthetic Data from Diffusion Models Improves ImageNet Classification},
  journal      = {Trans. Mach. Learn. Res.},
  volume       = {2023},
  year         = {2023},
}






@article{lee2016computationally,
  title={Computationally efficient truncated nuclear norm minimization for high dynamic range imaging},
  author={Lee, Chul and Lam, Edmund Y},
  journal={IEEE Transactions on Image Processing},
  volume={25},
  number={9},
  pages={4145--4157},
  year={2016},
  publisher={IEEE}
}

@article{goyal2018noise,
  title={Noise issues prevailing in various types of medical images},
  author={Goyal, Bhawna and Agrawal, Sunil and Sohi, BS},
  journal={Biomedical \& Pharmacology Journal},
  volume={11},
  number={3},
  pages={1227},
  year={2018},
  publisher={Biomedical and Pharmacology Journal}
}

@article{hussain2024exploring,
  title={Exploring the Impact of Noise and Image Quality on Deep Learning Performance in DXA Images},
  author={Hussain, Dildar and Hyeon Gu, Yeong},
  journal={Diagnostics},
  volume={14},
  number={13},
  pages={1328},
  year={2024},
  publisher={MDPI}
}


@article{gao2021noise,
  title={Noise robustness low-rank learning algorithm for electroencephalogram signal classification},
  author={Gao, Ming and Liu, Runmin and Mao, Jie},
  journal={Frontiers in Neuroscience},
  volume={15},
  pages={797378},
  year={2021},
  publisher={Frontiers Media SA}
}

@article{lu2015low,
  title={Low-rank preserving projections},
  author={Lu, Yuwu and Lai, Zhihui and Xu, Yong and Li, Xuelong and Zhang, David and Yuan, Chun},
  journal={IEEE transactions on cybernetics},
  volume={46},
  number={8},
  pages={1900--1913},
  year={2015},
  publisher={IEEE}
}


@inproceedings{ren2022robust,
  title={Robust low-rank convolution network for image denoising},
  author={Ren, Jiahuan and Zhang, Zhao and Hong, Richang and Xu, Mingliang and Zhang, Haijun and Zhao, Mingbo and Wang, Meng},
  booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
  pages={6211--6219},
  year={2022}
}

@article{hu2015large,
  title={Large scale multi-class classification with truncated nuclear norm regularization},
  author={Hu, Yao and Jin, Zhongming and Shi, Yi and Zhang, Debing and Cai, Deng and He, Xiaofei},
  journal={Neurocomputing},
  volume={148},
  pages={310--317},
  year={2015},
  publisher={Elsevier}
}

@inproceedings{zhang2017truncated,
  title={Truncated nuclear norm based low Rank Embedding},
  author={Zhang, Fanlong and Chang, Heyou and Yang, Guowei and Yang, Zhangjing and Wan, Minghua},
  booktitle={Biometric Recognition: 12th Chinese Conference, CCBR 2017, Shenzhen, China, October 28-29, 2017, Proceedings 12},
  pages={708--715},
  year={2017},
  organization={Springer}
}

%LRC
@article{bartlett2005,
	author = {Bartlett, Peter L. and Bousquet, Olivier and Mendelson, Shahar},
	fjournal = {The Annals of Statistics},
	journal = {Ann. Statist.},
	month = {08},
	number = {4},
	pages = {1497--1537},
	publisher = {The Institute of Mathematical Statistics},
	title = {Local Rademacher complexities},
	volume = {33},
	year = {2005},
}

@article{koltchinskii2006,
author = "Koltchinskii, Vladimir",
fjournal = "The Annals of Statistics",
journal = "Ann. Statist.",
month = "12",
number = "6",
pages = "2593--2656",
publisher = "The Institute of Mathematical Statistics",
title = "Local Rademacher complexities and oracle inequalities in risk minimization",
volume = "34",
year = "2006"
}

@inproceedings{Mendelson02-geometric-kernel-machine,
  author    = {Shahar Mendelson},
  title     = {Geometric Parameters of Kernel Machines},
  booktitle = {Conference on Learning Theory (COLT)},
  year      = {2002},
}


@inproceedings{trabucco2023effective,
  author       = {Brandon Trabucco and
                  Kyle Doherty and
                  Max Gurinas and
                  Ruslan Salakhutdinov},
  title        = {Effective Data Augmentation With Diffusion Models},
  booktitle    = {The Twelfth International Conference on Learning Representations,
                  {ICLR} 2024, Vienna, Austria, May 7-11, 2024},
  year         = {2024},
}

@article{azizi2023synthetic,
  title={Synthetic data from diffusion models improves imagenet classification},
  author={Azizi, Shekoofeh and Kornblith, Simon and Saharia, Chitwan and Norouzi, Mohammad and Fleet, David J},
  journal={Transactions on Machine Learning Research},
  year={2023}
}



@inproceedings{he2022synthetic,
  author       = {Ruifei He and
                  Shuyang Sun and
                  Xin Yu and
                  Chuhui Xue and
                  Wenqing Zhang and
                  Philip H. S. Torr and
                  Song Bai and
                  Xiaojuan Qi},
  title        = {Is Synthetic Data from Generative Models Ready for Image Recognition?},
  booktitle    = {The Eleventh International Conference on Learning Representations,
                  {ICLR} 2023, Kigali, Rwanda, May 1-5, 2023},
  year         = {2023},
}


@article{NaftaliIB,
  author       = {Naftali Tishby and
                  Fernando C. N. Pereira and
                  William Bialek},
  title        = {The information bottleneck method},
  journal      = {CoRR},
  volume       = {physics/0004057},
  year         = {2000},
}
@inproceedings{AlemiFD017,
  author       = {Alexander A. Alemi and
                  Ian Fischer and
                  Joshua V. Dillon and
                  Kevin Murphy},
  title        = {Deep Variational Information Bottleneck},
  booktitle    = {5th International Conference on Learning Representations, {ICLR} 2017,
                  Toulon, France, April 24-26, 2017, Conference Track Proceedings},
  publisher    = {OpenReview.net},
  year         = {2017},

}
@inproceedings{lai2021information,
  author       = {Qiuxia Lai and
                  Yu Li and
                  Ailing Zeng and
                  Minhao Liu and
                  Hanqiu Sun and
                  Qiang Xu},
  editor       = {Zhi{-}Hua Zhou},
  title        = {Information Bottleneck Approach to Spatial Attention Learning},
  booktitle    = {Proceedings of the Thirtieth International Joint Conference on Artificial
                  Intelligence, {IJCAI} 2021, Virtual Event / Montreal, Canada, 19-27
                  August 2021},
  pages        = {779--785},
  publisher    = {ijcai.org},
  year         = {2021},
}



@article{AmjadG20,
  author       = {Rana Ali Amjad and
                  Bernhard C. Geiger},
  title        = {Learning Representations for Neural Network-Based Classification Using
                  the Information Bottleneck Principle},
  journal      = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
  volume       = {42},
  number       = {9},
  pages        = {2225--2239},
  year         = {2020},
}
@inproceedings{KawaguchiDJH23,
  author       = {Kenji Kawaguchi and
                  Zhun Deng and
                  Xu Ji and
                  Jiaoyang Huang},
  editor       = {Andreas Krause and
                  Emma Brunskill and
                  Kyunghyun Cho and
                  Barbara Engelhardt and
                  Sivan Sabato and
                  Jonathan Scarlett},
  title        = {How Does Information Bottleneck Help Deep Learning?},
  booktitle    = {International Conference on Machine Learning, {ICML} 2023, 23-29 July
                  2023, Honolulu, Hawaii, {USA}},
  series       = {Proceedings of Machine Learning Research},
  volume       = {202},
  pages        = {16049--16096},
  publisher    = {{PMLR}},
  year         = {2023},
}


@article{MIBNet,
  author       = {Junxia Wang and
                  Yuanjie Zheng and
                  Jun Ma and
                  Xinmeng Li and
                  Chongjing Wang and
                  James Gee and
                  Haipeng Wang and
                  Wenhui Huang},
  title        = {Information bottleneck-based interpretable multitask network for breast
                  cancer classification and segmentation},
  journal      = {Medical Image Anal.},
  volume       = {83},
  pages        = {102687},
  year         = {2023},
  url          = {https://doi.org/10.1016/j.media.2022.102687},
  doi          = {10.1016/J.MEDIA.2022.102687},
  timestamp    = {Tue, 07 May 2024 20:23:17 +0200},
  biburl       = {https://dblp.org/rec/journals/mia/WangZMLWGWH23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}


@article{li2023ib,
  title={IB-TransUNet: combining information bottleneck and transformer for medical image segmentation},
  author={Li, Guangju and Jin, Dehu and Yu, Qi and Qi, Meng},
  journal={Journal of King Saud University-Computer and Information Sciences},
  volume={35},
  number={3},
  pages={249--258},
  year={2023},
  publisher={Elsevier}
}


@inproceedings{
schott2024information,
title={Information Bottleneck-based Feature Weighting for Enhanced Medical Image Out-of-Distribution Detection},
author={Brayden Schott and Zan Klanecek and Alison Deatsch and Victor Santoro-Fernandes and Thomas Francken and Scott Perlman and Robert Jeraj},
booktitle={Submitted to Uncertainty for Safe Utilization of Machine Learning in Medical Imaging - 6th International Workshop},
year={2024},
url={https://openreview.net/forum?id=Mshexk31gE},
note={under review}
}


@inproceedings{demir2021information,
  title={Information bottleneck attribution for visual explanations of diagnosis and prognosis},
  author={Demir, Ugur and Irmakci, Ismail and Keles, Elif and Topcu, Ahmet and Xu, Ziyue and Spampinato, Concetto and Jambawalikar, Sachin and Turkbey, Evrim and Turkbey, Baris and Bagci, Ulas},
  booktitle={Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 12},
  pages={396--405},
  year={2021},
  organization={Springer}
}

@inproceedings{zhang2021datasetgan,
  title={Datasetgan: Efficient labeled data factory with minimal human effort},
  author={Zhang, Yuxuan and Ling, Huan and Gao, Jun and Yin, Kangxue and Lafleche, Jean-Francois and Barriuso, Adela and Torralba, Antonio and Fidler, Sanja},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10145--10155},
  year={2021}
}


@inproceedings{li2022bigdatasetgan,
  title={BigDatasetGAN: Synthesizing ImageNet with pixel-wise annotations},
  author={Li, Daiqing and Ling, Huan and Kim, Seung Wook and Kreis, Karsten and Fidler, Sanja and Torralba, Antonio},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={21330--21340},
  year={2022}
}





@article{goodfellow2014generative,
  title={Generative adversarial nets},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  journal={Advances in neural information processing systems},
  volume={27},
  year={2014}
}

@article{saharia2022photorealistic,
  title={Photorealistic text-to-image diffusion models with deep language understanding},
  author={Saharia, Chitwan and Chan, William and Saxena, Saurabh and Li, Lala and Whang, Jay and Denton, Emily L and Ghasemipour, Kamyar and Gontijo Lopes, Raphael and Karagol Ayan, Burcu and Salimans, Tim and others},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={36479--36494},
  year={2022}
}



@article{lei2023image,
  title={Image Captions are Natural Prompts for Text-to-Image Models},
  author={Lei, Shiye and Chen, Hao and Zhang, Sen and Zhao, Bo and Tao, Dacheng},
  journal={arXiv preprint arXiv:2307.08526},
  year={2023}
}

@InProceedings{Sariyildiz_2023_CVPR,
    author    = {Sar{\i}y{\i}ld{\i}z, Mert B\"ulent and Alahari, Karteek and Larlus, Diane and Kalantidis, Yannis},
    title     = {Fake It Till You Make It: Learning Transferable Representations From Synthetic ImageNet Clones},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {8011-8021}
}


@article{vendrow2023dataset,
  title={Dataset interfaces: Diagnosing model failures using controllable counterfactual generation},
  author={Vendrow, Joshua and Jain, Saachi and Engstrom, Logan and Madry, Aleksander},
  journal={arXiv preprint arXiv:2302.07865},
  year={2023}
}


@article{bansal2023leaving,
  title={Leaving reality to imagination: Robust classification via generated datasets},
  author={Bansal, Hritik and Grover, Aditya},
  journal={arXiv preprint arXiv:2302.02503},
  year={2023}
}


@article{yuan2022not,
  title={Not just pretty pictures: Text-to-image generators enable interpretable interventions for robust representations},
  author={Yuan, Jianhao and Pinto, Francesco and Davies, Adam and Gupta, Aarushi and Torr, Philip},
  journal={arXiv preprint arXiv:2212.11237},
  year={2022}
}

@article{tian2023stablerep,
  title={StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners},
  author={Tian, Yonglong and Fan, Lijie and Isola, Phillip and Chang, Huiwen and Krishnan, Dilip},
  journal={arXiv preprint arXiv:2306.00984},
  year={2023}
}


@inproceedings{shin2018medical,
  title={Medical image synthesis for data augmentation and anonymization using generative adversarial networks},
  author={Shin, Hoo-Chang and Tenenholtz, Neil A and Rogers, Jameson K and Schwarz, Christopher G and Senjem, Matthew L and Gunter, Jeffrey L and Andriole, Katherine P and Michalski, Mark},
  booktitle={Simulation and Synthesis in Medical Imaging: Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings 3},
  pages={1--11},
  year={2018},
  organization={Springer}
}


@inproceedings{zhu2017unpaired,
  title={Unpaired image-to-image translation using cycle-consistent adversarial networks},
  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={2223--2232},
  year={2017}
}


@inproceedings{jiang2018tumor,
  title={Tumor-aware, adversarial domain adaptation from CT to MRI for lung cancer segmentation},
  author={Jiang, Jue and Hu, Yu-Chi and Tyagi, Neelam and Zhang, Pengpeng and Rimner, Andreas and Mageras, Gig S and Deasy, Joseph O and Veeraraghavan, Harini},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11},
  pages={777--785},
  year={2018},
  organization={Springer}
}


@article{sharma2019missing,
  title={Missing MRI pulse sequence synthesis using multi-modal generative adversarial network},
  author={Sharma, Anmol and Hamarneh, Ghassan},
  journal={IEEE transactions on medical imaging},
  volume={39},
  number={4},
  pages={1170--1183},
  year={2019},
  publisher={IEEE}
}


@inproceedings{akrout2023diffusion,
  title={Diffusion-based data augmentation for skin disease classification: Impact across original medical datasets to fully synthetic images},
  author={Akrout, Mohamed and Gyepesi, B{\'a}lint and Holl{\'o}, P{\'e}ter and Po{\'o}r, Adrienn and Kincs{\H{o}}, Bl{\'a}ga and Solis, Stephen and Cirone, Katrina and Kawahara, Jeremy and Slade, Dekker and Abid, Latif and others},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={99--109},
  year={2023},
  organization={Springer}
}

@inproceedings{zhong2024meddiffusion,
  title={Meddiffusion: Boosting health risk prediction via diffusion-based data augmentation},
  author={Zhong, Yuan and Cui, Suhan and Wang, Jiaqi and Wang, Xiaochen and Yin, Ziyi and Wang, Yaqing and Xiao, Houping and Huai, Mengdi and Wang, Ting and Ma, Fenglong},
  booktitle={Proceedings of the 2024 SIAM International Conference on Data Mining (SDM)},
  pages={499--507},
  year={2024},
  organization={SIAM}
}

@article{cha2020evaluation,
  title={Evaluation of data augmentation via synthetic images for improved breast mass detection on mammograms using deep learning},
  author={Cha, Kenny H and Petrick, Nicholas and Pezeshk, Aria and Graff, Christian G and Sharma, Diksha and Badal, Andreu and Sahiner, Berkman},
  journal={Journal of Medical Imaging},
  volume={7},
  number={1},
  pages={012703--012703},
  year={2020},
  publisher={Society of Photo-Optical Instrumentation Engineers}
}

@inproceedings{
na2024labelnoise,
title={Label-Noise Robust Diffusion Models},
author={Byeonghu Na and Yeongmin Kim and HeeSun Bae and Jung Hyun Lee and Se Jung Kwon and Wanmo Kang and Il-chul Moon},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=HXWTXXtHNl}
}


@article{zhou2023training,
  title={Training on Thin Air: Improve Image Classification with Generated Data},
  author={Zhou, Yongchao and Sahak, Hshmat and Ba, Jimmy},
  journal={arXiv preprint arXiv:2305.15316},
  year={2023}
}
@article{shu2019meta,
  title={Meta-weight-net: Learning an explicit mapping for sample weighting},
  author={Shu, Jun and Xie, Qi and Yi, Lixuan and Zhao, Qian and Zhou, Sanping and Xu, Zongben and Meng, Deyu},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@article{guo2022learning,
  title={Learning to re-weight examples with optimal transport for imbalanced classification},
  author={Guo, Dandan and Li, Zhuo and Zhao, He and Zhou, Mingyuan and Zha, Hongyuan and others},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={25517--25530},
  year={2022}
}


@inproceedings{jain2024learning,
  title={Learning model uncertainty as variance-minimizing instance weights},
  author={Jain, Nishant and Shanmugam, Karthikeyan and Shenoy, Pradeep},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024}
}


@inproceedings{ZhangP21,
  author       = {Zizhao Zhang and
                  Tomas Pfister},
  title        = {Learning Fast Sample Re-weighting Without Reward Data},
  booktitle    = {2021 {IEEE/CVF} International Conference on Computer Vision, {ICCV}
                  2021, Montreal, QC, Canada, October 10-17, 2021},
  pages        = {705--714},
  publisher    = {{IEEE}},
  year         = {2021},
}

@inproceedings{algan2021meta,
  title={Meta soft label generation for noisy labels},
  author={Algan, G{\"o}rkem and Ulusoy, Ilkay},
  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
  pages={7142--7148},
  year={2021},
  organization={IEEE}
}


@inproceedings{StableDiffusionLatent,
  author       = {Robin Rombach and
                  Andreas Blattmann and
                  Dominik Lorenz and
                  Patrick Esser and
                  Bj{\"{o}}rn Ommer},
  title        = {High-Resolution Image Synthesis with Latent Diffusion Models},
  booktitle    = {{IEEE/CVF} Conference on Computer Vision and Pattern Recognition,
                  {CVPR} 2022, New Orleans, LA, USA, June 18-24, 2022},
  pages        = {10674--10685},
  publisher    = {{IEEE}},
  year         = {2022},
}
@article{ho2022classifier,
  title={Classifier-free diffusion guidance},
  author={Ho, Jonathan and Salimans, Tim},
  journal={arXiv preprint arXiv:2207.12598},
  year={2022}
}


@inproceedings{Han2018NIPS,
  author       = {Bo Han and
                  Quanming Yao and
                  Xingrui Yu and
                  Gang Niu and
                  Miao Xu and
                  Weihua Hu and
                  Ivor W. Tsang and
                  Masashi Sugiyama},
  title        = {Co-teaching: Robust training of deep neural networks with extremely
                  noisy labels},
  booktitle    = {Advances in Neural Information Processing Systems 31: Annual Conference
                  on Neural Information Processing Systems 2018, NeurIPS 2018, December
                  3-8, 2018, Montr{\'{e}}al, Canada},
  pages        = {8536--8546},
  year         = {2018},
}


@inproceedings{Yu0YNTS19,
  author       = {Xingrui Yu and
                  Bo Han and
                  Jiangchao Yao and
                  Gang Niu and
                  Ivor W. Tsang and
                  Masashi Sugiyama},
  title        = {How does Disagreement Help Generalization against Label Corruption?},
  booktitle    = {Proceedings of the 36th International Conference on Machine Learning,
                  {ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
  series       = {Proceedings of Machine Learning Research},
  volume       = {97},
  pages        = {7164--7173},
  publisher    = {{PMLR}},
  year         = {2019},
}


@inproceedings{SongKPS021,
  author       = {Hwanjun Song and
                  Minseok Kim and
                  Dongmin Park and
                  Yooju Shin and
                  Jae{-}Gil Lee},
  editor       = {Feida Zhu and
                  Beng Chin Ooi and
                  Chunyan Miao},
  title        = {Robust Learning by Self-Transition for Handling Noisy Labels},
  booktitle    = {{KDD} '21: The 27th {ACM} {SIGKDD} Conference on Knowledge Discovery
                  and Data Mining, Virtual Event, Singapore, August 14-18, 2021},
  pages        = {1490--1500},
  publisher    = {{ACM}},
  year         = {2021},

}

@inproceedings{NguyenMNNBB20,
  author       = {Duc Tam Nguyen and
                  Chaithanya Kumar Mummadi and
                  Thi{-}Phuong{-}Nhung Ngo and
                  Thi Hoai Phuong Nguyen and
                  Laura Beggel and
                  Thomas Brox},
  title        = {{SELF:} Learning to Filter Noisy Labels with Self-Ensembling},
  booktitle    = {8th International Conference on Learning Representations, {ICLR} 2020,
                  Addis Ababa, Ethiopia, April 26-30, 2020},
  publisher    = {OpenReview.net},
  year         = {2020},

}
@article{SongKPSL23,
  author       = {Hwanjun Song and
                  Minseok Kim and
                  Dongmin Park and
                  Yooju Shin and
                  Jae{-}Gil Lee},
  title        = {Learning From Noisy Labels With Deep Neural Networks: {A} Survey},
  journal      = {{IEEE} Trans. Neural Networks Learn. Syst.},
  volume       = {34},
  number       = {11},
  pages        = {8135--8153},
  year         = {2023},
  url          = {https://doi.org/10.1109/TNNLS.2022.3152527},
  doi          = {10.1109/TNNLS.2022.3152527},
  timestamp    = {Thu, 09 Nov 2023 21:13:28 +0100},
  biburl       = {https://dblp.org/rec/journals/tnn/SongKPSL23.bib},
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@inproceedings{HeS0XZTBQ23,
  author       = {Ruifei He and
                  Shuyang Sun and
                  Xin Yu and
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                  Wenqing Zhang and
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                  Song Bai and
                  Xiaojuan Qi},
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  booktitle    = {The Eleventh International Conference on Learning Representations,
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  publisher    = {OpenReview.net},
  year         = {2023},
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@inproceedings{Wu0JMTL21,
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  pages        = {62--71},
  publisher    = {{IEEE}},
  year         = {2021},
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chhabra2024what,
title={''What Data Benefits My Classifier?'' Enhancing Model Performance and Interpretability through Influence-Based Data Selection},
author={Anshuman Chhabra and Peizhao Li and Prasant Mohapatra and Hongfu Liu},
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year={2024},
url={https://openreview.net/forum?id=HE9eUQlAvo}
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  author       = {Shaobo Lin and
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  title        = {Explore the Power of Synthetic Data on Few-shot Object Detection},
  booktitle    = {{IEEE/CVF} Conference on Computer Vision and Pattern Recognition,
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  pages        = {638--647},
  publisher    = {{IEEE}},
  year         = {2023},
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@inproceedings{campal,
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  author={Yang, Jianan and Wang, Haobo and Wu, Sai and Chen, Gang and Zhao, Junbo},
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  year={2023},
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  pages={16424--16440},
  year={2023},
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@INPROCEEDINGS{10301392,
  author={Kushnir, Dan and Venturi, Luca},
  booktitle={2023 International Conference on Sampling Theory and Applications (SampTA)}, 
  title={Diffusion-Based Sampling for Deep Active Learning}, 
  year={2023},
  volume={},
  number={},
  pages={1-9},
  keywords={Training;Geometry;Annotations;Training data;Artificial neural networks;Benchmark testing;Data models},
  doi={10.1109/SampTA59647.2023.10301392}}
