@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}
}

@article{azizi2022robust,
  title={Robust and efficient medical imaging with self-supervision},
  author={Azizi, Shekoofeh and Culp, Laura and Freyberg, Jan and Mustafa, Basil and Baur, Sebastien and Kornblith, Simon and Chen, Ting and MacWilliams, Patricia and Mahdavi, S Sara and Wulczyn, Ellery and others},
  journal={arXiv preprint arXiv:2205.09723},
  year={2022}
}

@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{xu2022revisiting,
  title={Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime},
  author={Xu, Xun and Liao, Jingyi and Cai, Lile and Nguyen, Manh Cuong and Lu, Kangkang and Zhang, Wanyue and Yazici, Yasin and Foo, Chuan Sheng},
  journal={arXiv preprint arXiv:2205.03001},
  year={2022}
}

@article{bertrand2019lateral,
  title={Do lateral views help automated chest x-ray predictions?},
  author={Bertrand, Hadrien and Hashir, Mohammad and Cohen, Joseph Paul},
  journal={arXiv preprint arXiv:1904.08534},
  year={2019}
}



@article{rajpurkar2020chexaid,
  title={CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV},
  author={Rajpurkar, Pranav and O’Connell, Chloe and Schechter, Amit and Asnani, Nishit and Li, Jason and Kiani, Amirhossein and Ball, Robyn L and Mendelson, Marc and Maartens, Gary and van Hoving, Dani{\"e}l J and others},
  journal={NPJ digital medicine},
  volume={3},
  number={1},
  pages={1--8},
  year={2020},
  publisher={Nature Publishing Group}
}

@inproceedings{rajpurkar2021chexternal,
  title={CheXternal: Generalization of deep learning models for chest X-ray interpretation to photos of chest X-rays and external clinical settings},
  author={Rajpurkar, Pranav and Joshi, Anirudh and Pareek, Anuj and Ng, Andrew Y and Lungren, Matthew P},
  booktitle={Proceedings of the Conference on Health, Inference, and Learning},
  pages={125--132},
  year={2021}
}

@article{rajpurkar2017chexnet,
  title={Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning},
  author={Rajpurkar, Pranav and Irvin, Jeremy and Zhu, Kaylie and Yang, Brandon and Mehta, Hershel and Duan, Tony and Ding, Daisy and Bagul, Aarti and Langlotz, Curtis and Shpanskaya, Katie and others},
  journal={arXiv preprint arXiv:1711.05225},
  year={2017}
}

@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}
}

@article{rajpurkar2018deep,
  title={Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists},
  author={Rajpurkar, Pranav and Irvin, Jeremy and Ball, Robyn L and Zhu, Kaylie and Yang, Brandon and Mehta, Hershel and Duan, Tony and Ding, Daisy and Bagul, Aarti and Langlotz, Curtis P and others},
  journal={PLoS medicine},
  volume={15},
  number={11},
  pages={e1002686},
  year={2018},
  publisher={Public Library of Science San Francisco, CA USA}
}

@inproceedings{li2018thoracic,
  title={Thoracic disease identification and localization with limited supervision},
  author={Li, Zhe and Wang, Chong and Han, Mei and Xue, Yuan and Wei, Wei and Li, Li-Jia and Fei-Fei, Li},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={8290--8299},
  year={2018}
}

@article{cohen2019chester,
  title={Chester: A web delivered locally computed chest x-ray disease prediction system},
  author={Cohen, Joseph Paul and Bertin, Paul and Frappier, Vincent},
  journal={arXiv preprint arXiv:1901.11210},
  year={2019}
}

@article{rubin2018large,
  title={Large scale automated reading of frontal and lateral chest x-rays using dual convolutional neural networks},
  author={Rubin, Jonathan and Sanghavi, Deepan and Zhao, Claire and Lee, Kathy and Qadir, Ashequl and Xu-Wilson, Minnan},
  journal={arXiv preprint arXiv:1804.07839},
  year={2018}
}

@inproceedings{hashir2020quantifying,
  title={Quantifying the value of lateral views in deep learning for chest x-rays},
  author={Hashir, Mohammad and Bertrand, Hadrien and Cohen, Joseph Paul},
  booktitle={Medical Imaging with Deep Learning},
  pages={288--303},
  year={2020},
  organization={PMLR}
}

@incollection{hosseinzadeh2021systematic,
  title={A systematic benchmarking analysis of transfer learning for medical image analysis},
  author={Hosseinzadeh Taher, Mohammad Reza and Haghighi, Fatemeh and Feng, Ruibin and Gotway, Michael B and Liang, Jianming},
  booktitle={Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health},
  pages={3--13},
  year={2021},
  publisher={Springer}
}
@article{guan2018diagnose,
  title={Diagnose like a radiologist: Attention guided convolutional neural network for thorax disease classification},
  author={Guan, Qingji and Huang, Yaping and Zhong, Zhun and Zheng, Zhedong and Zheng, Liang and Yang, Yi},
  journal={arXiv preprint arXiv:1801.09927},
  year={2018}
}

@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{baltruschat2019comparison,
  title={Comparison of deep learning approaches for multi-label chest X-ray classification},
  author={Baltruschat, Ivo M and Nickisch, Hannes and Grass, Michael and Knopp, Tobias and Saalbach, Axel},
  journal={Scientific reports},
  volume={9},
  number={1},
  pages={1--10},
  year={2019},
  publisher={Nature Publishing Group}
}

@article{guan2018diagnose,
  title={Diagnose like a radiologist: Attention guided convolutional neural network for thorax disease classification},
  author={Guan, Qingji and Huang, Yaping and Zhong, Zhun and Zheng, Zhedong and Zheng, Liang and Yang, Yi},
  journal={arXiv preprint arXiv:1801.09927},
  year={2018}
}

%1
@article{setio2016pulmonary,
  title={Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks},
  author={Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Litjens, Geert and Gerke, Paul and Jacobs, Colin and Van Riel, Sarah J and Wille, Mathilde Marie Winkler and Naqibullah, Matiullah and S{\'a}nchez, Clara I and Van Ginneken, Bram},
  journal={IEEE transactions on medical imaging},
  volume={35},
  number={5},
  pages={1160--1169},
  year={2016},
  publisher={IEEE}
}
%2
@article{shachor2020mixture,
  title={A mixture of views network with applications to multi-view medical imaging},
  author={Shachor, Yaniv and Greenspan, Hayit and Goldberger, Jacob},
  journal={Neurocomputing},
  volume={374},
  pages={1--9},
  year={2020},
  publisher={Elsevier}
}
%3
@article{geras2017high,
  title={High-resolution breast cancer screening with multi-view deep convolutional neural networks},
  author={Geras, Krzysztof J and Wolfson, Stacey and Shen, Yiqiu and Wu, Nan and Kim, S and Kim, Eric and Heacock, Laura and Parikh, Ujas and Moy, Linda and Cho, Kyunghyun},
  journal={arXiv preprint arXiv:1703.07047},
  year={2017}
}
%4

%5
@article{carneiro2017automated,
  title={Automated analysis of unregistered multi-view mammograms with deep learning},
  author={Carneiro, Gustavo and Nascimento, Jacinto and Bradley, Andrew P},
  journal={IEEE transactions on medical imaging},
  volume={36},
  number={11},
  pages={2355--2365},
  year={2017},
  publisher={IEEE}
}
%6
@inproceedings{kyono2019multi,
  title={Multi-view multi-task learning for improving autonomous mammogram diagnosis},
  author={Kyono, Trent and Gilbert, Fiona J and Schaar, Mihaela},
  booktitle={Machine Learning for Healthcare Conference},
  pages={571--591},
  year={2019},
  organization={PMLR}
}
%7
@article{khan2019multi,
  title={Multi-view feature fusion based four views model for mammogram classification using convolutional neural network},
  author={Khan, Hasan Nasir and Shahid, Ahmad Raza and Raza, Basit and Dar, Amir Hanif and Alquhayz, Hani},
  journal={IEEE Access},
  volume={7},
  pages={165724--165733},
  year={2019},
  publisher={IEEE}
}
%8
@inproceedings{bermejo2018emphysema,
  title={Emphysema classification using a multi-view convolutional network},
  author={Bermejo-Pel{\'a}ez, David and Estepar, Ra{\'u}l San Jos{\'e} and Ledesma-Carbayo, Mar{\'\i}a J},
  booktitle={2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},
  pages={519--522},
  year={2018},
  organization={IEEE}
}
%9
@article{kitamura2019ankle,
  title={Ankle fracture detection utilizing a convolutional neural network ensemble implemented with a small sample, de novo training, and multiview incorporation},
  author={Kitamura, Gene and Chung, Chul Y and Moore, Barry E},
  journal={Journal of digital imaging},
  volume={32},
  number={4},
  pages={672--677},
  year={2019},
  publisher={Springer}
}
%10
@inproceedings{li2019mvp,
  title={MVP-Net: multi-view FPN with position-aware attention for deep universal lesion detection},
  author={Li, Zihao and Zhang, Shu and Zhang, Junge and Huang, Kaiqi and Wang, Yizhou and Yu, Yizhou},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={13--21},
  year={2019},
  organization={Springer}
}
%11
@article{rubin2018large,
  title={Large scale automated reading of frontal and lateral chest x-rays using dual convolutional neural networks},
  author={Rubin, Jonathan and Sanghavi, Deepan and Zhao, Claire and Lee, Kathy and Qadir, Ashequl and Xu-Wilson, Minnan},
  journal={arXiv preprint arXiv:1804.07839},
  year={2018}
}
%12
@article{bertrand2019lateral,
  title={Do lateral views help automated chest x-ray predictions?},
  author={Bertrand, Hadrien and Hashir, Mohammad and Cohen, Joseph Paul},
  journal={arXiv preprint arXiv:1904.08534},
  year={2019}
}
%13
@inproceedings{hashir2020quantifying,
  title={Quantifying the value of lateral views in deep learning for chest x-rays},
  author={Hashir, Mohammad and Bertrand, Hadrien and Cohen, Joseph Paul},
  booktitle={Medical Imaging with Deep Learning},
  pages={288--303},
  year={2020},
  organization={PMLR}
}
%14
@article{lopez2022multi,
  title={Multi-View Breast Cancer Classification via Hypercomplex Neural Networks},
  author={Lopez, Eleonora and Grassucci, Eleonora and Valleriani, Martina and Comminiello, Danilo},
  journal={arXiv preprint arXiv:2204.05798},
  year={2022}
}
%15
@inproceedings{zhu2021mvc,
  title={Mvc-net: Multi-view chest radiograph classification network with deep fusion},
  author={Zhu, Xiongfeng and Feng, Qianjin},
  booktitle={2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  pages={554--558},
  year={2021},
  organization={IEEE}
}
%16
@inproceedings{cohen2020limits,
  title={On the limits of cross-domain generalization in automated X-ray prediction},
  author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, Rupert and Bertrand, Hadrien},
  booktitle={Medical Imaging with Deep Learning},
  pages={136--155},
  year={2020},
  organization={PMLR}
}
%17
@article{liz2022deep,
  title={Deep learning for understanding multilabel imbalanced Chest X-ray datasets},
  author={Liz, Helena and Huertas-Tato, Javier and S{\'a}nchez-Monta{\~n}{\'e}s, Manuel and Del Ser, Javier and Camacho, David},
  journal={arXiv preprint arXiv:2207.14408},
  year={2022}
}
%18
@inproceedings{zbontar2021barlow,
  title={Barlow twins: Self-supervised learning via redundancy reduction},
  author={Zbontar, Jure and Jing, Li and Misra, Ishan and LeCun, Yann and Deny, St{\'e}phane},
  booktitle={International Conference on Machine Learning},
  pages={12310--12320},
  year={2021},
  organization={PMLR}
}
%19
@article{grill2020bootstrap,
  title={Bootstrap your own latent-a new approach to self-supervised learning},
  author={Grill, Jean-Bastien and Strub, Florian and Altch{\'e}, Florent and Tallec, Corentin and Richemond, Pierre and Buchatskaya, Elena and Doersch, Carl and Avila Pires, Bernardo and Guo, Zhaohan and Gheshlaghi Azar, Mohammad and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={21271--21284},
  year={2020}
}
%20
@inproceedings{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={International conference on machine learning},
  pages={1597--1607},
  year={2020},
  organization={PMLR}
}
%21

@article{khan2023revisiting,
  title={Revisiting Fine-Tuning Strategies for Self-supervised Medical Imaging Analysis},
  author={Khan, Muhammad Osama and Fang, Yi},
  journal={arXiv preprint arXiv:2307.10915},
  year={2023}
}

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


@inproceedings{irvin2019chexpert,
  title={Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison},
  author={Irvin, Jeremy and Rajpurkar, Pranav and Ko, Michael and Yu, Yifan and Ciurea-Ilcus, Silviana and Chute, Chris and Marklund, Henrik and Haghgoo, Behzad and Ball, Robyn and Shpanskaya, Katie and others},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={33},
  number={01},
  pages={590--597},
  year={2019}
}

@article{johnson2019mimic,
  title={MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs},
  author={Johnson, Alistair EW and Pollard, Tom J and Greenbaum, Nathaniel R and Lungren, Matthew P and Deng, Chih-ying and Peng, Yifan and Lu, Zhiyong and Mark, Roger G and Berkowitz, Seth J and Horng, Steven},
  journal={arXiv preprint arXiv:1901.07042},
  year={2019}
}

@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{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={2009 IEEE conference on computer vision and pattern recognition},
  pages={248--255},
  year={2009},
  organization={Ieee}
}


% InsDis [40]
@inproceedings{wu2018unsupervised,
  title={Unsupervised feature learning via non-parametric instance discrimination},
  author={Wu, Zhirong and Xiong, Yuanjun and Yu, Stella X and Lin, Dahua},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={3733--3742},
  year={2018}
}


% , MoCo-v1 [19],
@inproceedings{he2020momentum,
  title={Momentum contrast for unsupervised visual representation learning},
  author={He, Kaiming and Fan, Haoqi and Wu, Yuxin and Xie, Saining and Girshick, Ross},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={9729--9738},
  year={2020}
}
% PCL-v1 [26]
@article{li2020prototypical,
  title={Prototypical contrastive learning of unsupervised representations},
  author={Li, Junnan and Zhou, Pan and Xiong, Caiming and Hoi, Steven CH},
  journal={arXiv preprint arXiv:2005.04966},
  year={2020}
}
% , PIRL [27],
@inproceedings{misra2020self,
  title={Self-supervised learning of pretext-invariant representations},
  author={Misra, Ishan and Maaten, Laurens van der},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6707--6717},
  year={2020}
}
% PCL-v2 [26]
@article{li2020prototypical,
  title={Prototypical contrastive learning of unsupervised representations},
  author={Li, Junnan and Zhou, Pan and Xiong, Caiming and Hoi, Steven CH},
  journal={arXiv preprint arXiv:2005.04966},
  year={2020}
}
% SimCLR-v1 [9]
@inproceedings{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={International conference on machine learning},
  pages={1597--1607},
  year={2020},
  organization={PMLR}
}
% , MoCo-v2 [11]
@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}
}
% , SeLa-v2 [7]
@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={Advances in Neural Information Processing Systems},
  volume={33},
  pages={9912--9924},
  year={2020}
}
% , SimCLR-v2 [10],
@article{chen2020big,
  title={Big self-supervised models are strong semi-supervised learners},
  author={Chen, Ting and Kornblith, Simon and Swersky, Kevin and Norouzi, Mohammad and Hinton, Geoffrey E},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={22243--22255},
  year={2020}
}
% InfoMin [36] check
@article{tian2020makes,
  title={What makes for good views for contrastive learning?},
  author={Tian, Yonglong and Sun, Chen and Poole, Ben and Krishnan, Dilip and Schmid, Cordelia and Isola, Phillip},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={6827--6839},
  year={2020}
}

% ) BYOL [14],
@article{grill2020bootstrap,
  title={Bootstrap your own latent-a new approach to self-supervised learning},
  author={Grill, Jean-Bastien and Strub, Florian and Altch{\'e}, Florent and Tallec, Corentin and Richemond, Pierre and Buchatskaya, Elena and Doersch, Carl and Avila Pires, Bernardo and Guo, Zhaohan and Gheshlaghi Azar, Mohammad and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={21271--21284},
  year={2020}
}
% DeepCluster-v2, [7]
@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={Advances in Neural Information Processing Systems},
  volume={33},
  pages={9912--9924},
  year={2020}
}
% SwAV [7], check
@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={Advances in Neural Information Processing Systems},
  volume={33},
  pages={9912--9924},
  year={2020}
}
% and Barlow Twins [41]
@inproceedings{zbontar2021barlow,
  title={Barlow twins: Self-supervised learning via redundancy reduction},
  author={Zbontar, Jure and Jing, Li and Misra, Ishan and LeCun, Yann and Deny, St{\'e}phane},
  booktitle={International Conference on Machine Learning},
  pages={12310--12320},
  year={2021},
  organization={PMLR}
}

% Chest Xray 14
@inproceedings{wang2017chestx,
  title={Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases},
  author={Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald M},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2097--2106},
  year={2017}
}

% CheXpert
@inproceedings{irvin2019chexpert,
  title={Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison},
  author={Irvin, Jeremy and Rajpurkar, Pranav and Ko, Michael and Yu, Yifan and Ciurea-Ilcus, Silviana and Chute, Chris and Marklund, Henrik and Haghgoo, Behzad and Ball, Robyn and Shpanskaya, Katie and others},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={33},
  number={01},
  pages={590--597},
  year={2019}
}

@article{azizi2022robust,
  title={Robust and Efficient Medical Imaging with Self-Supervision},
  author={Azizi, Shekoofeh and Culp, Laura and Freyberg, Jan and Mustafa, Basil and Baur, Sebastien and Kornblith, Simon and Chen, Ting and MacWilliams, Patricia and Mahdavi, S Sara and Wulczyn, Ellery and others},
  journal={arXiv preprint arXiv:2205.09723},
  year={2022}
}

@inproceedings{wang2019characterizing,
  title={Characterizing and avoiding negative transfer},
  author={Wang, Zirui and Dai, Zihang and P{\'o}czos, Barnab{\'a}s and Carbonell, Jaime},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={11293--11302},
  year={2019}
}

@inproceedings{havaei2016hemis,
  title={Hemis: Hetero-modal image segmentation},
  author={Havaei, Mohammad and Guizard, Nicolas and Chapados, Nicolas and Bengio, Yoshua},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={469--477},
  year={2016},
  organization={Springer}
}

@article{johnson2019mimic,
  title={MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs},
  author={Johnson, Alistair EW and Pollard, Tom J and Greenbaum, Nathaniel R and Lungren, Matthew P and Deng, Chih-ying and Peng, Yifan and Lu, Zhiyong and Mark, Roger G and Berkowitz, Seth J and Horng, Steven},
  journal={arXiv preprint arXiv:1901.07042},
  year={2019}
}

@inproceedings{wu2020improving,
  title={Improving the ability of deep neural networks to use information from multiple views in breast cancer screening},
  author={Wu, Nan and Jastrz{\k{e}}bski, Stanis{\l}aw and Park, Jungkyu and Moy, Linda and Cho, Kyunghyun and Geras, Krzysztof J},
  booktitle={Medical Imaging with Deep Learning},
  pages={827--842},
  year={2020},
  organization={PMLR}
}

@inproceedings{wu2022characterizing,
  title={Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks},
  author={Wu, Nan and Jastrzebski, Stanislaw and Cho, Kyunghyun and Geras, Krzysztof J},
  booktitle={International Conference on Machine Learning},
  pages={24043--24055},
  year={2022},
  organization={PMLR}
}

% MAE
@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}
}