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@article{pin2022comparative,
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@article{liu2019development,
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@article{Muchuchuti2023,
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@article{Khan2021,
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@article{Li2022,
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@article{Zhou2022,
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@article{Wang2022,
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@article{chan2023artificial,
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% Long form of conference & journal abbreviations -- especially for camera ready
@String(IJCV  = {Int. J. Comput. Vis.})
@String(CVPR  = {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV  = {Int. Conf. Comput. Vis.})
@String(ECCV  = {Eur. Conf. Comput. Vis.})
@String(NeurIPS = {Adv. Neural Inform. Process. Syst.})
@String(ICML  = {Int. Conf. Mach. Learn.})
@String(ICLR  = {Int. Conf. Learn. Represent.})
@String(ACCV  = {Asian Conf. Comput. Vis.})
@String(BMVC  = {Brit. Mach. Vis. Conf.})
@String(CVPRW = {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(AAAI  = {AAAI})
@String(IJCAI = {IJCAI})
@String(ICIP  = {IEEE Int. Conf. Image Process.})
@String(ICPR  = {Int. Conf. Pattern Recog.})
@String(ICASSP=	{ICASSP})
@String(ICME  = {Int. Conf. Multimedia and Expo})
@String(JMLR  = {J. Mach. Learn. Res.})
@String(TMLR  = {Trans. Mach. Learn Res.})
@String(TOG   = {ACM Trans. Graph.})
@String(TIP   = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TCSVT = {IEEE Trans. Circuit Syst. Video Technol.})
@String(TMM   = {IEEE Trans. Multimedia})
@String(ACMMM = {ACM Int. Conf. Multimedia})
@String(PR    = {Pattern Recognition})

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


% Short form of conference & journal abbreviations -- especially for submission version
% if desired, remove these macros in favor of the above ones
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NeurIPS = {NeurIPS})
@String(ICML  = {ICML})
@String(ICLR  = {ICLR})
@String(ACCV  = {ACCV})
@String(BMVC  =	{BMVC})
@String(CVPRW = {CVPRW})
@String(AAAI  = {AAAI})
@String(IJCAI = {IJCAI})
@String(ICIP  = {ICIP})
@String(ICPR  = {ICPR})
@String(ICASSP=	{ICASSP})
@String(ICME  =	{ICME})
@String(JMLR  = {JMLR})
@String(TMLR  = {TMLR})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(PR    = {PR})










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}
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  year={2002},
  publisher={Wiley Online Library}
}
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  journal={Investigative ophthalmology \\& visual science},
  volume={44},
  number={2},
  pages={761--766},
  year={2003},
  publisher={The Association for Research in Vision and Ophthalmology}
}
@article{niemeijer2005automatic,
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  publisher={IEEE}
}
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  year={2008},
  publisher={IEEE}
}
@article{abramoff2010automated,
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}
@inproceedings{zhao2019data,
  title={Data-driven enhancement of blurry retinal images via generative adversarial networks},
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  pages={75--83},
  year={2019},
  organization={Springer}
}
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  year={2017}
}
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}
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}
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}
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  organization={Springer}
}
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  year={2017},
  organization={Springer}
}
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  pages={4866--4871},
  year={2011},
  publisher={The Association for Research in Vision and Ophthalmology}
}
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  year={2016},
  organization={IEEE}
}
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  pages={193--200},
  year={2015},
  publisher={IEEE}
}
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@article{decenciere_feedback_2014,
	title = {Feedback on a publicly distributed database: the Messidor database},
	volume = {33},
	copyright = {Copyright (c) 2014 Image Analysis \& Stereology},
	issn = {1854-5165},
	shorttitle = {Feedback on a publicly distributed database},
	url = {http://www.ias-iss.org/ojs/IAS/article/view/1155},
	doi = {10.5566/ias.1155},
	abstract = {The Messidor database, which contains hundreds of eye fundus images, has been publicly distributed since 2008. It was created by the Messidor project in order to evaluate automatic lesion segmentation and diabetic retinopathy grading methods. Designing, producing and maintaining such a database entails significant costs. By publicly sharing it, one hopes to bring a valuable resource to the public research community. However, the real interest and benefit of the research community is not easy to quantify. We analyse here the feedback on the Messidor database, after more than 6 years of diffusion. This analysis should apply to other similar research databases.},
	language = {en},
	number = {3},
	journal = {Image Analysis \& Stereology},
	author = {Decenci{\`e}re, Etienne and Zhang, Xiwei and Cazuguel, Guy and Lay, Bruno and Cochener, B{\'e}atrice and Trone, Caroline and Gain, Philippe and Ordonez, Richard and Massin, Pascale and Erginay, Ali and Charton, B{\'e}atrice and Klein, Jean-Claude},
	month = aug,
	year = {2014},
	keywords = {Diabetic Retinopathy, image database, Image Processing, Messidor},
	pages = {231--234},
}

@misc{RFMID,
doi = {10.21227/s3g7-st65},
url = {https://dx.doi.org/10.21227/s3g7-st65},
author = {Pachade, Samiksha and Porwal, Prasanna and Thulkar, Dhanshree and Kokare, Manesh and Deshmukh, Girish and Sahasrabuddhe, Vivek and Giancardo, Luca and Quellec, Gwenol{\'e} and M{\'e}riaudeau, Fabrice},
publisher = {IEEE Dataport},
title = {Retinal Fundus Multi-disease Image Dataset (RFMiD)},
year = {2020} }


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  author={Caputi, Luigi and Pidnebesna, Anna and Hlinka, Jaroslav},
  journal={NeuroImage},
  volume={238},
  pages={118245},
  year={2021},
  publisher={Elsevier}
}

@inproceedings{stucki2023topologically,
  title={Topologically faithful image segmentation via induced matching of persistence barcodes},
  author={Stucki, Nico and Paetzold, Johannes C and Shit, Suprosanna and Menze, Bjoern and Bauer, Ulrich},
  booktitle={{ICML}},
  year={2023},
  organization={PMLR}
}

@article{somasundaram2021persistent,
  title={Persistent homology of tumor CT scans is associated with survival in lung cancer},
  author={Somasundaram, Eashwar and others},
  journal={Medical physics},
  volume={48},
  number={11},
  pages={7043--7051},
  year={2021},
  publisher={Wiley Online Library}
}

@article{zia2023topological,
  title={Topological deep learning: a review of an emerging paradigm},
  author={Zia, Ali and others},
  journal={Artif. Intell. Rev.},
  year={2024}
}

@article{papamarkou2024position,
  title={Position Paper: Challenges and Opportunities in Topological Deep Learning},
  author={Papamarkou, Theodore and Birdal, Tolga and Bronstein, Michael and Carlsson, Gunnar and others},
  journal={arXiv preprint arXiv:2402.08871},
  year={2024}
}

@article{petri2014homological,
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@inproceedings{haft2020topological,
  title={A topological encoding {CNN} for segmentation of {3D} multiphoton images of brain vasculature},
  author={Haft-Javaherian, Mohammad and others},
  booktitle={{CVPR} Workshops},
  pages={990--991},
  year={2020}
}

@article{clough2020topological,
  title={A topological loss function for deep-learning based image segmentation using persistent homology},
  author={Clough, James R and Byrne, Nicholas and Oksuz, Ilkay and Zimmer, Veronika A and Schnabel, Julia A and King, Andrew P},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={44},
  number={12},
  pages={8766--8778},
  year={2020},
  publisher={IEEE}
}
@article{grandini2020metrics,
  title={Metrics for multi-class classification: an overview},
  author={Grandini, Margherita and Bagli, Enrico and Visani, Giorgio},
  journal={arXiv preprint arXiv:2008.05756},
  year={2020}
}
@misc{DONUT,
  author = {Giunti, Barbara and Lazovskis, J{\=a}nis and Rieck, Bastian},
  title  = {
    {DONUT}: {D}atabase of {O}riginal \& {N}on-{T}heoretical {U}ses of {T}opology
  },
  note   = {\url{https://donut.topology.rocks}},
  year   = {2022},
  key    = {DONUT},
}

@inproceedings{ahmed2023tofi,
  title={ToFi-ML: Retinal Image Screening with Topological Machine Learning},
  author={Ahmed, Faisal and Coskunuzer, Baris},
  booktitle={Annual Conference on Medical Image Understanding and Analysis},
  pages={281--297},
  year={2023},
  organization={Springer}
}

@inproceedings{yadav2023histopathological,
  title={Histopathological Cancer Detection with Topological Signatures},
  author={Yadav, Ankur and Ahmed, Faisal and Daescu, Ovidiu and Gedik, Reyhan and Coskunuzer, Baris},
  booktitle={{IEEE BIBM}},
  pages={1610--1619},
  year={2023},
  organization={IEEE}
}

@inproceedings{wong2021persistent,
  title={Persistent homology based graph convolution network for fine-grained {3D} shape segmentation},
  author={Wong, Chi-Chong and Vong, Chi-Man},
  booktitle={{ICCV}},
  pages={7098--7107},
  year={2021}
}






@article{gomez2023bus,
  title={BUS-BRA: A breast ultrasound dataset for assessing computer-aided diagnosis systems},
  author={G{\'o}mez-Flores, Wilfrido and Gregorio-Calas, Maria Julia and Coelho de Albuquerque Pereira, Wagner},
  journal={Medical Physics},
  year={2023},
  publisher={Wiley Online Library}
}


@article{rodrigues2017breast,
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  year={2017}
}

@article{qu2022vgg,
  title={A VGG attention vision transformer network for benign and malignant classification of breast ultrasound images},
  author={Qu, Xiaolei and Lu, Hongyan and Tang, Wenzhong and Wang, Shuai and Zheng, Dezhi and Hou, Yaxin and Jiang, Jue},
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  year={2022},
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}

@article{ayana2022buvitnet,
  title={BUVITNET: Breast ultrasound detection via vision transformers},
  author={Ayana, Gelan and Choe, Se-Woon},
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  pages={2654},
  year={2022},
  publisher={MDPI}
}

@article{benhammou2020breakhis,
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  year={2020},
  publisher={Elsevier}
}

@inproceedings{gheflati2022vision,
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  author={Gheflati, Behnaz and Rivaz, Hassan},
  booktitle={2022 44th Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
  pages={480--483},
  year={2022},
  organization={IEEE}
}

@inproceedings{he2016deep,
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@inproceedings{spanhol2017deep,
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  year={2017},
  organization={IEEE}
}
@inproceedings{spanhol2016breast,
  title={Breast cancer histopathological image classification using {CNN}s},
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  booktitle={{IJCNN}},
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}

@article{zhao2022multi,
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  journal={arXiv preprint arXiv:2207.06799},
  year={2022}
}

@misc{breakhis2024,
    author       = {Spanhol, F. and Oliveira, L. S. and Petitjean, C. and Heutte, L.},
    title        = {Breast Cancer Histopathological Database (BreakHis)},
    year         = 2024,
    url          = {https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/}
}


@inproceedings{tan2019efficientnet,
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  author={Tan, Mingxing and Le, Quoc},
  booktitle={{ICML}},
  pages={6105--6114},
  year={2019},
  organization={PMLR}
}


@inproceedings{szegedy2017inception,
  title={Inception-v4, inception-resnet and the impact of residual connections on learning},
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  booktitle={Proceedings of the AAAI conference on artificial intelligence},
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  number={1},
  year={2017}
}

@article{howard2017mobilenets,
  title={Mobilenets: Efficient convolutional neural networks for mobile vision applications},
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  year={2017}
}

@article{farahani2022deep,
  title={Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images},
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  year={2022},
  publisher={Elsevier}
}

@article{asadi2024machine,
  title={Machine Learning-driven Histotype Diagnosis of Ovarian Carcinoma: Insights from the OCEAN AI Challenge},
  author={Asadi-Aghbolaghi, Maryam and  others},
  journal={medRxiv},
  pages={2024--04},
  year={2024},
  publisher={Cold Spring Harbor Laboratory Press}
}
@misc{UBC-OCEAN,
    author = {Ali Bashashati and others},
    title = {{UBC} Ovarian Cancer Subtype Classification and Outlier Detection ({UBC-OCEAN})},
    publisher = {Kaggle},
    year = {2023},
    url = {https://kaggle.com/competitions/UBC-OCEAN}
}

@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  booktitle={{CVPR}},
  pages={4700--4708},
  year={2017}
}
@article{krizhevsky2012imagenet,
  title={Imagenet classification with deep convolutional neural networks},
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@article{sabour2017dynamic,
  title={Dynamic routing between capsules},
  author={Sabour, Sara and Frosst, Nicholas and Hinton, Geoffrey E},
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}

@article{wang2021automatic,
  title={Automatic classification of breast cancer histopathological images based on deep feature fusion},
  author={Wang, Pin and others},
  journal={Biomedical Signal Processing and Control},
  volume={65},
  pages={102341},
  year={2021},
  publisher={Elsevier}
}

@article{saida2022diagnosing,
  title={Diagnosing ovarian cancer on MRI: a preliminary study comparing deep learning and radiologist assessments},
  author={Saida, Tsukasa and Mori, Kensaku and Hoshiai, Sodai and Sakai, Masafumi and Urushibara, Aiko and Ishiguro, Toshitaka and Minami, Manabu and Satoh, Toyomi and Nakajima, Takahito},
  journal={Cancers},
  volume={14},
  number={4},
  pages={987},
  year={2022},
  publisher={MDPI}
}

@inproceedings{simonyan2015very,
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  booktitle={{ICLR}},
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}


@article{sharmin2024dataset,
  title={A dataset of color fundus images for the detection and classification of eye diseases},
  author={Sharmin, Shayla and Rashid, Mohammad Riadur and Khatun, Tania and Hasan, Md Zahid and Uddin, Mohammad Shorif and others},
  journal={Data in Brief},
  volume={57},
  pages={110979},
  year={2024},
  publisher={Elsevier}
}

@article{mcguirl2020topological,
  author       = {McGuirl, Melissa R. and Volkening, Alexandria and Sandstede, Bj\"orn},
  title        = {Topological Data Analysis of Zebrafish Patterns},
  journal      = {{PNAS}},
  volume       = {117},
  number       = {10},
  pages        = {5113--5124},
  year         = {2020}
}

@article{bremer2018topological,
  author       = {Saggar, Manish and others},
  title        = {Towards a New Approach to Reveal Dynamical Organization of the Brain Using Topological Data Analysis},
  journal      = {Nature Communications},
  volume       = {9},
  number       = {1},
  pages        = {1399},
  year         = {2018}
 
}
@article{patel2021artificial,
  author  = {Patel, Shalin and others},
  title   = {Artificial Intelligence in Dermatology: A Systematic Review of Applications and Challenges},
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  volume  = {28},
  number  = {8},
  pages   = {1842--1857},
  year    = {2021}
}

@article{lum2013extracting,
  author       = {Lum, Pek and others},
  title        = {Extracting Insights from the Shape of Complex Data Using Topology},
  journal      = {Scientific Reports},
  volume       = {3},
  pages        = {1236},
  year         = {2013},
}

@article{skaf2022topological,
  author       = {Skaf, Yara and Laubenbacher, Reinhard C.},
  title        = {Topological Data Analysis in Biomedicine: A Review},
  journal      = {Journal of Biomedical Informatics},
  volume       = {130},
  pages        = {104082},
  year         = {2022},
  doi          = {10.1016/j.jbi.2022.104082},
}



@article{adams2017persistence,
  author       = {Adams, Henry and others},
  title        = {Persistence Images: A Stable Vector Representation of Persistent Homology},
  journal      = {Journal of Machine Learning Research},
  volume       = {18},
  number       = {8},
  pages        = {1--35},
  year         = {2017},
}

@inproceedings{kahle2021topological,
  author       = {Kahle, Ronald and Rieck, Bastian and Moor, Michael and Horn, Max},
  title        = {Topological Regularization for Deep Image Segmentation},
  booktitle    = {Proceedings of the IEEE International Symposium on Biomedical Imaging},
  pages        = {235--239},
  year         = {2021},
}
@article{maurya2024hybrid,
  title={Hybrid topological data analysis and deep learning for basal cell carcinoma diagnosis},
  author={Maurya, Akanksha and  others},
  journal={Journal of Imaging Informatics in Medicine},
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  number={1},
  pages={92--106},
  year={2024},
  publisher={Springer}
}
@article{elyasi2022classification,
  title={Classification of skin lesions by Tda alongside Xception neural network},
  author={Elyasi, Naiereh and Hosseini Moghadam, M},
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  number={3},
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  year={2022},
  publisher={Shahrood University of Technology}
}
@inproceedings{chung2018topological,
  title={Topological approaches to skin disease image analysis},
  author={Chung, Yu-Min and Hu, Chuan-Shen and Lawson, Austin and Smyth, Clifford},
  booktitle={2018 IEEE International Conference on Big Data (Big Data)},
  pages={100--105},
  year={2018},
  organization={IEEE}
}
@inproceedings{santhirasekaram2023topology,
  author       = {Santhirasekaram, Ainkaran and Winkler, Maximilian and Rockall, Anne and Glocker, Ben},
  title        = {Topology Preserving Compositionality for Robust Medical Image Segmentation},
  booktitle    = {Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Workshops (CVPRW)},
  pages        = {999--1008},
  year         = {2023},
}

@article{chacholski2019topological,
  author       = {Chach{\'o}lski, Wojciech and Hiraoka, Yasuaki and Obayashi, Ippei},
  title        = {Topological Data Analysis for Complex Medical Imaging},
  journal      = {IEEE Transactions on Medical Imaging},
  volume       = {38},
  number       = {6},
  pages        = {1389--1398},
  year         = {2019}
}

@article{johnson2022application,
  author       = {Johnson, Kevin and S\"oderkvist, Johan and Hansson, Patrik},
  title        = {Application of Persistent Homology in Histopathological Grading},
  journal      = {Computers in Biology and Medicine},
  volume       = {145},
  pages        = {105379},
  year         = {2022},
}
@article{esteva2017dermatologist,
  author  = {Esteva, Andre and others},
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  year    = {2017}
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@article{menegola2017knowledge,
  author  = {Menegola, Andrea and Tavares, Jonas M.~R.~S. and Fornaciali, Marcos and Li, Liao and Avila, Sandra and Valle, Eduardo},
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  year    = {2017},
}

@article{brinker2019convolutional,
  author  = {Brinker, T.~Jan and others},
  title   = {{CNN} Outperforms Dermatologists in Melanoma Classification: A Prospective, Multicenter, Noninferiority Study},
  journal = {JAMA Dermatology},
  volume  = {155},
  number  = {11},
  pages   = {922--926},
  year    = {2019},

}




@article{tschandl2018ham10000,
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@inproceedings{mehta2022mobilevit,
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}
@inproceedings{ding2022davit,
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  organization={Springer}
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@article{medmnistv2,
  title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification},
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  journal={Scientific Data},
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  year={2023},
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}
@InProceedings{Sandler2018MobileNetV2,
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  author    = {Sandler, Mark and others},
  booktitle = {{CVPR}},
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  doi       = {10.1109/CVPR.2018.00474}
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@article{mendonca2013ph2,
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  publisher={IEEE}
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@article{giotis2015mednode,
  title={MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images},
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@article{daneshjou2022disparities,
  title={Disparities in dermatology AI performance on a diverse, curated clinical image set},
  author={Daneshjou, Roxana and others},
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  volume={8},
  number={6},
  pages={eabj7790},
  year={2022},
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@inproceedings{korkmaz2025cumperlay,
  title={CuMPerLay: Learning Cubical Multiparameter Persistence Vectorizations},
  author={Korkmaz, Caner and Nuwagira, Brighton and Coskunuzer, Baris and Birdal, Tolga},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={27084--27094},
  year={2025}
}

@article{loiseaux2023stable,
  title={Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures},
  author={Loiseaux, David and Scoccola, Luis and Carri{\`e}re, Mathieu and Botnan, Magnus Bakke and Oudot, Steve},
  journal={NeurIPS},
  year={2023}
}
@inproceedings{du2022distilling,
  title={Distilling knowledge from topological representations for pathological complete response prediction},
  author={Du, Shiyi and Lao, Qicheng and Kang, Qingbo and Li, Yiyue and Jiang, Zekun and Zhao, Yanfeng and Li, Kang},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={56--65},
  year={2022},
  organization={Springer}
}
@inproceedings{garin2019topological,
  title={A topological" reading" lesson: Classification of MNIST using TDA},
  author={Garin, Ad{\'e}lie and Tauzin, Guillaume},
  booktitle={2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)},
  pages={1551--1556},
  year={2019},
  organization={IEEE}
}
@article{yang2021medmnist,
  title     = {MedMNIST v2: A large-scale lightweight benchmark for 2D and 3D biomedical image classification},
  author    = {Yang, Jun and Shi, Rui and Wei, Donglai and Mu, Guotai and Zhang, Yu and Gao, Mingchen and Chen, Li and Li, Xinxing and Li, Qing and Zhao, Bo and others},
  journal   = {Scientific Data},
  volume    = {10},
  number    = {1},
  pages     = {41},
  year      = {2023},
  publisher = {Nature Publishing Group}
}



@inproceedings{codella2019isic,
  title     = {Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the International Skin Imaging Collaboration (ISIC)},
  author    = {Codella, Noel C. F. and Rotemberg, Veronica and Tschandl, Philipp and Emre Celebi, M. and Dusza, Stephen W. and Gutman, David and Helba, Brian and Kalloo, Aadi and Liopyris, Konstantinos and Marchetti, Michael A. and Kittler, Harald and Halpern, Allan C.},
  booktitle = {arXiv preprint arXiv:1902.03368},
  year      = {2019}
}

@article{pacheco2020pad,
  title     = {PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones},
  author    = {Pacheco, Andre Gustavo C. and Lima, Gustavo R. and Salomão, Andre S. and Krohling, Renato A. and dos Santos, Neusa M. M. and Krohling, Ricardo A.},
  journal   = {Data in Brief},
  volume    = {32},
  pages     = {106221},
  year      = {2020},
  publisher = {Elsevier}
}
@inproceedings{howard2019mobilenetv3,
  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 Le, Quoc V. and Adam, Hartwig},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  pages     = {1314--1324},
  year      = {2019}
}

@inproceedings{huang2017densenet,
  title     = {Densely Connected Convolutional Networks},
  author    = {Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q.},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages     = {4700--4708},
  year      = {2017}
}

@inproceedings{he2016resnet,
  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 (CVPR)},
  pages     = {770--778},
  year      = {2016}
}

@inproceedings{tan2021efficientnetv2,
  title     = {EfficientNetV2: Smaller Models and Faster Training},
  author    = {Tan, Mingxing and Le, Quoc V.},
  booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
  pages     = {10096--10106},
  year      = {2021}
}

@inproceedings{dosovitskiy2021vit,
  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 Uszkoreit, Jakob and Houlsby, Neil},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2021}
}

@inproceedings{liu2021swin,
  title     = {Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
  author    = {Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  pages     = {10012--10022},
  year      = {2021}
}

