@inproceedings{futrega2021optimized,
  title={Optimized U-Net for brain tumor segmentation},
  author={Futrega, Micha{\l} and Milesi, Alexandre and Marcinkiewicz, Micha{\l} and Ribalta, Pablo},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={15--29},
  year={2021},
  organization={Springer}
}

@article{abderezaei20223d,
  title={3{D} {I}nception-{B}ased {T}rans{M}orph: {P}re-and {P}ost-operative {M}ulti-contrast {MRI} {R}egistration in {B}rain {T}umors},
  author={Abderezaei, Javid and Pionteck, Aymeric and Chopra, Agamdeep and Kurt, Mehmet},
  journal={arXiv preprint arXiv:2212.04579},
  year={2022}
}

@ARTICLE{diff-feature-clustering,
  author={Kim, Wonjik and Kanezaki, Asako and Tanaka, Masayuki},
  journal={IEEE Transactions on Image Processing}, 
  title={Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering}, 
  year={2020},
  volume={29},
  number={},
  pages={8055-8068},
  doi={10.1109/TIP.2020.3011269}}

@InProceedings{blob-loss,
author="Kofler, Florian
and Shit, Suprosanna
and Ezhov, Ivan
and Fidon, Lucas
and Horvath, Izabela
and Al-Maskari, Rami
and Li, Hongwei Bran
and Bhatia, Harsharan
and Loehr, Timo
and Piraud, Marie
and Erturk, Ali
and Kirschke, Jan
and Peeken, Jan C.
and Vercauteren, Tom
and Zimmer, Claus
and Wiestler, Benedikt
and Menze, Bjoern",
editor="Frangi, Alejandro
and de Bruijne, Marleen
and Wassermann, Demian
and Navab, Nassir",
title="blob loss: Instance Imbalance Aware Loss Functions for Semantic Segmentation",
booktitle="Information Processing in Medical Imaging",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="755--767",
isbn="978-3-031-34048-2"
}

@inproceedings{jadon2020survey,
  title={A survey of loss functions for semantic segmentation},
  author={Jadon, Shruti},
  booktitle={2020 IEEE conference on computational intelligence in bioinformatics and computational biology (CIBCB)},
  pages={1--7},
  year={2020},
  organization={IEEE}
}

@article{karimi2019reducing,
  title={Reducing the hausdorff distance in medical image segmentation with convolutional neural networks},
  author={Karimi, Davood and Salcudean, Septimiu E},
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  volume={39},
  number={2},
  pages={499--513},
  year={2019},
  publisher={IEEE}
}

@software{Silversmith_cc3d_Connected_components_2021,
author = {Silversmith, William},
doi = {https://zenodo.org/record/5535251},
month = sep,
title = {{cc3d: Connected components on multilabel 3D & 2D images.}},
version = {3.2.1},
year = {2021}
}

@inproceedings{optuna_2019,
    title={Optuna: A Next-generation Hyperparameter Optimization Framework},
    author={Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
    booktitle={Proceedings of the 25th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining},
    year={2019}
}




@inproceedings{Luu2022,
  title={Extending nn-UNet for brain tumor segmentation},
  author={Luu, Huan Minh and Park, Sung-Hong},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={173--186},
  year={2021},
  organization={Springer}
}



@ARTICLE{6975210,
  author={Menze, Bjoern H. and Jakab, Andras and Bauer, Stefan and Kalpathy-Cramer, Jayashree and Farahani, Keyvan and Kirby, Justin and Burren, Yuliya and Porz, Nicole and Slotboom, Johannes and Wiest, Roland and Lanczi, Levente and Gerstner, Elizabeth and Weber, Marc-André and Arbel, Tal and Avants, Brian B. and Ayache, Nicholas and Buendia, Patricia and Collins, D. Louis and Cordier, Nicolas and Corso, Jason J. and Criminisi, Antonio and Das, Tilak and Delingette, Hervé and Demiralp, Çağatay and Durst, Christopher R. and Dojat, Michel and Doyle, Senan and Festa, Joana and Forbes, Florence and Geremia, Ezequiel and Glocker, Ben and Golland, Polina and Guo, Xiaotao and Hamamci, Andac and Iftekharuddin, Khan M. and Jena, Raj and John, Nigel M. and Konukoglu, Ender and Lashkari, Danial and Mariz, José António and Meier, Raphael and Pereira, Sérgio and Precup, Doina and Price, Stephen J. and Raviv, Tammy Riklin and Reza, Syed M. S. and Ryan, Michael and Sarikaya, Duygu and Schwartz, Lawrence and Shin, Hoo-Chang and Shotton, Jamie and Silva, Carlos A. and Sousa, Nuno and Subbanna, Nagesh K. and Szekely, Gabor and Taylor, Thomas J. and Thomas, Owen M. and Tustison, Nicholas J. and Unal, Gozde and Vasseur, Flor and Wintermark, Max and Ye, Dong Hye and Zhao, Liang and Zhao, Binsheng and Zikic, Darko and Prastawa, Marcel and Reyes, Mauricio and Van Leemput, Koen},
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@article{GLI-3rdSource,
author = {Bakas, Spyridon and Akbari, Hamed and Sotiras, Aristeidis and Bilello, Michel and Rozycki, Martin and Kirby, Justin and Freymann, John and Farahani, Keyvan and Davatzikos, Christos},
year = {2017},
month = {09},
pages = {},
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}


@unknown{Wolleb2021,
author = {Wolleb, Julia and Sandkuehler, Robin and Bieder, Florentin and Valmaggia, Philippe and Cattin, Philippe},
year = {2021},
month = {12},
pages = {},
title = {Diffusion Models for Implicit Image Segmentation Ensembles}
}

@article{xing2023diff,
  title={Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation},
  author={Xing, Zhaohu and Wan, Liang and Fu, Huazhu and Yang, Guang and Zhu, Lei},
  journal={arXiv preprint arXiv:2303.10326},
  year={2023}
}

@article{ho2020denoising,
  title={Denoising diffusion probabilistic models},
  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={6840--6851},
  year={2020}
}

@inproceedings{cciccek20163d,
  title={3D U-Net: learning dense volumetric segmentation from sparse annotation},
  author={{\c{C}}i{\c{c}}ek, {\"O}zg{\"u}n and Abdulkadir, Ahmed and Lienkamp, Soeren S and Brox, Thomas and Ronneberger, Olaf},
  booktitle={Medical Image Computing and Computer-Assisted Intervention--MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19},
  pages={424--432},
  year={2016},
  organization={Springer}
}

@inproceedings{hatamizadeh2022unetr,
  title={Unetr: Transformers for 3d medical image segmentation},
  author={Hatamizadeh, Ali and Tang, Yucheng and Nath, Vishwesh and Yang, Dong and Myronenko, Andriy and Landman, Bennett and Roth, Holger R and Xu, Daguang},
  booktitle={Proceedings of the IEEE/CVF winter conference on applications of computer vision},
  pages={574--584},
  year={2022}
}

@inproceedings{hatamizadeh2021swin,
  title={Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images},
  author={Hatamizadeh, Ali and Nath, Vishwesh and Tang, Yucheng and Yang, Dong and Roth, Holger R and Xu, Daguang},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={272--284},
  year={2021},
  organization={Springer}
}

@misc{adewole2023brain,
      title={The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)}, 
      author={Maruf Adewole and Jeffrey D. Rudie and Anu Gbadamosi and Oluyemisi Toyobo and Confidence Raymond and Dong Zhang and Olubukola Omidiji and Rachel Akinola and Mohammad Abba Suwaid and Adaobi Emegoakor and Nancy Ojo and Kenneth Aguh and Chinasa Kalaiwo and Gabriel Babatunde and Afolabi Ogunleye and Yewande Gbadamosi and Kator Iorpagher and Evan Calabrese and Mariam Aboian and Marius Linguraru and Jake Albrecht and Benedikt Wiestler and Florian Kofler and Anastasia Janas and Dominic LaBella and Anahita Fathi Kzerooni and Hongwei Bran Li and Juan Eugenio Iglesias and Keyvan Farahani and James Eddy and Timothy Bergquist and Verena Chung and Russell Takeshi Shinohara and Walter Wiggins and Zachary Reitman and Chunhao Wang and Xinyang Liu and Zhifan Jiang and Ariana Familiar and Koen Van Leemput and Christina Bukas and Maire Piraud and Gian-Marco Conte and Elaine Johansson and Zeke Meier and Bjoern H Menze and Ujjwal Baid and Spyridon Bakas and Farouk Dako and Abiodun Fatade and Udunna C Anazodo},
      year={2023},
      eprint={2305.19369},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}



@misc{labella2023asnrmiccai,
      title={The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma}, 
      author={Dominic LaBella and Maruf Adewole and Michelle Alonso-Basanta and Talissa Altes and Syed Muhammad Anwar and Ujjwal Baid and Timothy Bergquist and Radhika Bhalerao and Sully Chen and Verena Chung and Gian-Marco Conte and Farouk Dako and James Eddy and Ivan Ezhov and Devon Godfrey and Fathi Hilal and Ariana Familiar and Keyvan Farahani and Juan Eugenio Iglesias and Zhifan Jiang and Elaine Johanson and Anahita Fathi Kazerooni and Collin Kent and John Kirkpatrick and Florian Kofler and Koen Van Leemput and Hongwei Bran Li and Xinyang Liu and Aria Mahtabfar and Shan McBurney-Lin and Ryan McLean and Zeke Meier and Ahmed W Moawad and John Mongan and Pierre Nedelec and Maxence Pajot and Marie Piraud and Arif Rashid and Zachary Reitman and Russell Takeshi Shinohara and Yury Velichko and Chunhao Wang and Pranav Warman and Walter Wiggins and Mariam Aboian and Jake Albrecht and Udunna Anazodo and Spyridon Bakas and Adam Flanders and Anastasia Janas and Goldey Khanna and Marius George Linguraru and Bjoern Menze and Ayman Nada and Andreas M Rauschecker and Jeff Rudie and Nourel Hoda Tahon and Javier Villanueva-Meyer and Benedikt Wiestler and Evan Calabrese},
      year={2023},
      eprint={2305.07642},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}



@article{baid2021rsnaasnrmiccai,
  title={The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification},
  author={Baid, Ujjwal and Ghodasara, Satyam and Mohan, Suyash and Bilello, Michel and Calabrese, Evan and Colak, Errol and Farahani, Keyvan and Kalpathy-Cramer, Jayashree and Kitamura, Felipe C and Pati, Sarthak and others},
  journal={arXiv preprint arXiv:2107.02314},
  year={2021}
}



@article{siddiquee2021redundancy,
  title={Redundancy reduction in semantic segmentation of 3D brain tumor MRIS},
  author={Siddiquee, Md Mahfuzur Rahman and Myronenko, Andriy},
  journal={arXiv preprint arXiv:2111.00742},
  year={2021}
}


@article{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},
  journal={arXiv preprint arXiv:2010.11929},
  year={2020}
}

@article{wu2022medsegdiff,
  title={Medsegdiff: Medical image segmentation with diffusion probabilistic model},
  author={Wu, Junde and Fang, Huihui and Zhang, Yu and Yang, Yehui and Xu, Yanwu},
  journal={arXiv preprint arXiv:2211.00611},
  year={2022}
}


@inproceedings{wolleb2022diffusion,
  title={Diffusion models for implicit image segmentation ensembles},
  author={Wolleb, Julia and Sandk{\"u}hler, Robin and Bieder, Florentin and Valmaggia, Philippe and Cattin, Philippe C},
  booktitle={International Conference on Medical Imaging with Deep Learning},
  pages={1336--1348},
  year={2022},
  organization={PMLR}
}

@article{ddpm,
  title={Denoising diffusion probabilistic models},
  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={6840--6851},
  year={2020}
}


@InProceedings{improved_ddpm,
  title = 	 {Improved Denoising Diffusion Probabilistic Models},
  author =       {Nichol, Alexander Quinn and Dhariwal, Prafulla},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {8162--8171},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v139/nichol21a/nichol21a.pdf},
  url = 	 {https://proceedings.mlr.press/v139/nichol21a.html},
  abstract = 	 {Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code and pre-trained models at https://github.com/openai/improved-diffusion.}
}



@INPROCEEDINGS{9077067,
  author={Ahuja, Sakshi and Panigrahi, B.K. and Gandhi, Tapan},
  booktitle={2020 International Conference on Contemporary Computing and Applications (IC3A)}, 
  title={Transfer Learning Based Brain Tumor Detection and Segmentation using Superpixel Technique}, 
  year={2020},
  volume={},
  number={},
  pages={244-249},
  doi={10.1109/IC3A48958.2020.233306}}


@article{baid2021rsna,
  title={The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification},
  author={Baid, Ujjwal and Ghodasara, Satyam and Mohan, Suyash and Bilello, Michel and Calabrese, Evan and Colak, Errol and Farahani, Keyvan and Kalpathy-Cramer, Jayashree and Kitamura, Felipe C and Pati, Sarthak and others},
  journal={arXiv preprint arXiv:2107.02314},
  year={2021}
}

@article{ostrom2018cbtrus,
  title={CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2011--2015},
  author={Ostrom, Quinn T and Gittleman, Haley and Truitt, Gabrielle and Boscia, Alexander and Kruchko, Carol and Barnholtz-Sloan, Jill S},
  journal={Neuro-oncology},
  volume={20},
  number={suppl\_4},
  pages={iv1--iv86},
  year={2018},
  publisher={Oxford University Press US}
}


@article{tan2020management,
  title={Management of glioblastoma: State of the art and future directions},
  author={Tan, Aaron C and Ashley, David M and L{\'o}pez, Giselle Y and Malinzak, Michael and Friedman, Henry S and Khasraw, Mustafa},
  journal={CA: a cancer journal for clinicians},
  volume={70},
  number={4},
  pages={299--312},
  year={2020},
  publisher={Wiley Online Library}
}

@article{hanif2017glioblastoma,
  title={Glioblastoma multiforme: a review of its epidemiology and pathogenesis through clinical presentation and treatment},
  author={Hanif, Farina and Muzaffar, Kanza and Perveen, Kahkashan and Malhi, Saima M and Simjee, Shabana U},
  journal={Asian Pacific journal of cancer prevention: APJCP},
  volume={18},
  number={1},
  pages={3},
  year={2017},
  publisher={Shahid Beheshti University of Medical Sciences}
}

@article{keunen2014multimodal,
  title={Multimodal imaging of gliomas in the context of evolving cellular and molecular therapies},
  author={Keunen, Olivier and Taxt, Torfinn and Gr{\"u}ner, Renate and Lund-Johansen, Morten and Tonn, Joerg-Christian and Pavlin, Tina and Bjerkvig, Rolf and Niclou, Simone P and Thorsen, Frits},
  journal={Advanced drug delivery reviews},
  volume={76},
  pages={98--115},
  year={2014},
  publisher={Elsevier}
}

@article{van2019perfusion,
  title={Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques},
  author={van Dijken, Bart RJ and van Laar, Peter Jan and Smits, Marion and Dankbaar, Jan Willem and Enting, Roelien H and van der Hoorn, Anouk},
  journal={Journal of Magnetic Resonance Imaging},
  volume={49},
  number={1},
  pages={11--22},
  year={2019},
  publisher={Wiley Online Library}
}


@article{wang2019advance,
  title={What is the advance of extent of resection in glioblastoma surgical treatment—a systematic review},
  author={Wang, Lei and Liang, Buqing and Li, Yan Icy and Liu, Xiang and Huang, Jason and Li, Yan Michael},
  journal={Chinese neurosurgical journal},
  volume={5},
  number={1},
  pages={1--6},
  year={2019},
  publisher={BioMed Central}
}

@article{yeghiazaryan2018family,
  title={Family of boundary overlap metrics for the evaluation of medical image segmentation},
  author={Yeghiazaryan, Varduhi and Voiculescu, Irina},
  journal={Journal of Medical Imaging},
  volume={5},
  number={1},
  pages={015006--015006},
  year={2018},
  publisher={Society of Photo-Optical Instrumentation Engineers}
}

@article{VNet,
  author       = {Fausto Milletari and Nassir Navab and Seyed{-}Ahmad Ahmadi},
  title        = {V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation},
  journal      = {CoRR},
  volume       = {abs/1606.04797},
  year         = {2016},
  url          = {http://arxiv.org/abs/1606.04797},
  eprinttype    = {arXiv},
  eprint       = {1606.04797},
  timestamp    = {Mon, 13 Aug 2018 16:48:14 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/MilletariNA16.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{UNetPP,
  author       = {Zongwei Zhou and Md Mahfuzur Rahman Siddiquee and Nima Tajbakhsh and Jianming Liang},
  title        = {UNet++: {A} Nested U-Net Architecture for Medical Image Segmentation},
  journal      = {CoRR},
  volume       = {abs/1807.10165},
  year         = {2018},
  url          = {http://arxiv.org/abs/1807.10165},
  eprinttype    = {arXiv},
  eprint       = {1807.10165},
  timestamp    = {Mon, 13 Aug 2018 16:46:03 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1807-10165.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{AttUNet,
  author       = {Ozan Oktay and
                  Jo Schlemper and
                  Lo{\"{\i}}c Le Folgoc and
                  Matthew C. H. Lee and
                  Mattias P. Heinrich and
                  Kazunari Misawa and
                  Kensaku Mori and
                  Steven G. McDonagh and
                  Nils Y. Hammerla and
                  Bernhard Kainz and
                  Ben Glocker and
                  Daniel Rueckert},
  title        = {Attention U-Net: Learning Where to Look for the Pancreas},
  journal      = {CoRR},
  volume       = {abs/1804.03999},
  year         = {2018},
  url          = {http://arxiv.org/abs/1804.03999},
  eprinttype    = {arXiv},
  eprint       = {1804.03999},
  timestamp    = {Tue, 17 Sep 2019 14:15:15 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1804-03999.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{3DUNet,
  author       = {{\"{O}}zg{\"{u}}n {\c{C}}i{\c{c}}ek and
                  Ahmed Abdulkadir and
                  Soeren S. Lienkamp and
                  Thomas Brox and
                  Olaf Ronneberger},
  title        = {3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation},
  journal      = {CoRR},
  volume       = {abs/1606.06650},
  year         = {2016},
  url          = {http://arxiv.org/abs/1606.06650},
  eprinttype    = {arXiv},
  eprint       = {1606.06650},
  timestamp    = {Mon, 13 Aug 2018 16:47:29 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/CicekALBR16.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@software{Honey_An_Optimization_Framework_2024,
author = {Honey, Ethan and Ren, Tianyi},
month = feb,
title = {{An Optimization Framework for Processing and Transfer   Learning for the Brain Tumor Segmentation}},
year = {2024}
}

@article{warfield2004simultaneous,
  title={Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation},
  author={Warfield, Simon K and Zou, Kelly H and Wells, William M},
  journal={IEEE transactions on medical imaging},
  volume={23},
  number={7},
  pages={903--921},
  year={2004},
  publisher={IEEE}
}


@article{isensee2021nnu,
  title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={Nature methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021},
  publisher={Nature Publishing Group}
}

@article{VDM,
  author       = {Diederik P. Kingma and
                  Tim Salimans and
                  Ben Poole and
                  Jonathan Ho},
  title        = {Variational Diffusion Models},
  journal      = {CoRR},
  volume       = {abs/2107.00630},
  year         = {2021},
  url          = {https://arxiv.org/abs/2107.00630},
  eprinttype    = {arXiv},
  eprint       = {2107.00630},
  timestamp    = {Wed, 07 Jul 2021 15:23:11 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2107-00630.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DMBGOIS,
  author       = {Prafulla Dhariwal and
                  Alex Nichol},
  title        = {Diffusion Models Beat GANs on Image Synthesis},
  journal      = {CoRR},
  volume       = {abs/2105.05233},
  year         = {2021},
  url          = {https://arxiv.org/abs/2105.05233},
  eprinttype    = {arXiv},
  eprint       = {2105.05233},
  timestamp    = {Fri, 14 May 2021 12:13:30 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2105-05233.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}


@article{ren2024optimization,
  title={An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation},
  author={Ren, Tianyi and Honey, Ethan and Rebala, Harshitha and Sharma, Abhishek and Chopra, Agamdeep and Kurt, Mehmet},
  journal={arXiv preprint arXiv:2402.07008},
  year={2024}
}

@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}
}

@inproceedings{cao2022swin,
  title={Swin-unet: Unet-like pure transformer for medical image segmentation},
  author={Cao, Hu and Wang, Yueyue and Chen, Joy and Jiang, Dongsheng and Zhang, Xiaopeng and Tian, Qi and Wang, Manning},
  booktitle={European conference on computer vision},
  pages={205--218},
  year={2022},
  organization={Springer}
}