% universal model
@inproceedings{liu2023clip,
  title={Clip-driven universal model for organ segmentation and tumor detection},
  author={Liu, Jie and Zhang, Yixiao and Chen, Jie-Neng and Xiao, Junfei and Lu, Yongyi and A Landman, Bennett and Yuan, Yixuan and Yuille, Alan and Tang, Yucheng and Zhou, Zongwei},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={21152--21164},
  year={2023}
}

@inproceedings{silva2023towards,
  title={Towards foundation models and few-shot parameter-efficient fine-tuning for volumetric organ segmentation},
  author={Silva-Rodr{\'\i}guez, Julio and Dolz, Jose and Ayed, Ismail Ben},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={213--224},
  year={2023},
  organization={Springer}
}
@inproceedings{ulrich2023multitalent,
  title={Multitalent: A multi-dataset approach to medical image segmentation},
  author={Ulrich, Constantin and Isensee, Fabian and Wald, Tassilo and Zenk, Maximilian and Baumgartner, Michael and Maier-Hein, Klaus H},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={648--658},
  year={2023},
  organization={Springer}
}

%MSD dataset
@article{antonelli2022medical,
  title={The medical segmentation decathlon},
  author={Antonelli, Michela and Reinke, Annika and Bakas, Spyridon and Farahani, Keyvan and Kopp-Schneider, Annette and Landman, Bennett A and Litjens, Geert and Menze, Bjoern and Ronneberger, Olaf and Summers, Ronald M and others},
  journal={Nature communications},
  volume={13},
  number={1},
  pages={4128},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

%GFNet
@article{rao2021global,
  title={Global Filter Networks for Image Classification},
  author={Rao, Yongming and Zhao, Wenliang and Zhu, Zheng and Lu, Jiwen and Zhou, Jie},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}


%PUNETR
@article{fischer2024prompt,
  title={Prompt tuning for parameter-efficient medical image segmentation},
  author={Fischer, Marc and Bartler, Alexander and Yang, Bin},
  journal={Medical Image Analysis},
  volume={91},
  pages={103024},
  year={2024},
  publisher={Elsevier}
}

% Adapting Pre-trained
@article{Zhang2023AdaptingPV,
  title={Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation},
  author={Yuhui Zhang and Shihua Huang and Zhengping Zhou and Matthew P. Lungren and Serena Yeung},
  journal={ArXiv},
  year={2023},
  volume={abs/2302.04303},
  url={https://api.semanticscholar.org/CorpusID:256697277}
}

% Aim
@inproceedings{yang2023aim,
    title={AIM: Adapting Image Models for Efficient Video Understanding},
    author={Taojiannan Yang and Yi Zhu and Yusheng Xie and Aston Zhang 
    and Chen Chen and Mu Li},
    booktitle={International Conference on Learning Representations},
    year={2023},
    url={https://openreview.net/forum?id=CIoSZ_HKHS7}
}
% DePT: Decoupled Prompt Tuning
@article{zhang2023dept,
  title={DePT: Decoupled Prompt Tuning},
  author={Zhang, Ji and Wu, Shihan and Gao, Lianli and Shen, Hengtao and Song, Jingkuan},
  journal={arXiv preprint arXiv:2309.07439},
  year={2023}
}

% Ladder side-tuning(LST)
@article{sung2022lst,
  title={Lst: Ladder side-tuning for parameter and memory efficient transfer learning},
  author={Sung, Yi-Lin and Cho, Jaemin and Bansal, Mohit},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={12991--13005},
  year={2022}
}

% Side Adapter Network(SAN)
@inproceedings{xu2023side,
  title={Side adapter network for open-vocabulary semantic segmentation},
  author={Xu, Mengde and Zhang, Zheng and Wei, Fangyun and Hu, Han and Bai, Xiang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2945--2954},
  year={2023}
}

@article{Feichtenhofer2020X3DEA,
  title={X3D: Expanding Architectures for Efficient Video Recognition},
  author={Christoph Feichtenhofer},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={200-210}
}
@article{Chollet2016XceptionDL,
  title={Xception: Deep Learning with Depthwise Separable Convolutions},
  author={François Chollet},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={1800-1807}
}

% DW Conv
@inproceedings{ye20193d,
  title={3d depthwise convolution: Reducing model parameters in 3d vision tasks},
  author={Ye, Rongtian and Liu, Fangyu and Zhang, Liqiang},
  booktitle={Advances in Artificial Intelligence: 32nd Canadian Conference on Artificial Intelligence, Canadian AI 2019, Kingston, ON, Canada, May 28--31, 2019, Proceedings 32},
  pages={186--199},
  year={2019},
  organization={Springer}
}

% Discrete Fourier Transform is separable
@article{Cooley1965AnAF,
  title={An algorithm for the machine calculation of complex Fourier series},
  author={James W. Cooley and John W. Tukey},
  journal={Mathematics of Computation},
  year={1965},
  volume={19},
  pages={297-301}
}

% FFT in vision
@inproceedings{yang2020fda,
  title={Fda: Fourier domain adaptation for semantic segmentation},
  author={Yang, Yanchao and Soatto, Stefano},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={4085--4095},
  year={2020}
}
@inproceedings{ding2017circnn,
  title={Circnn: accelerating and compressing deep neural networks using block-circulant weight matrices},
  author={Ding, Caiwen and Liao, Siyu and Wang, Yanzhi and Li, Zhe and Liu, Ning and Zhuo, Youwei and Wang, Chao and Qian, Xuehai and Bai, Yu and Yuan, Geng and others},
  booktitle={Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture},
  pages={395--408},
  year={2017}
}
@article{chi2020fast,
  title={Fast fourier convolution},
  author={Chi, Lu and Jiang, Borui and Mu, Yadong},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={4479--4488},
  year={2020}
}
@inproceedings{li2020falcon,
  title={Falcon: A fourier transform based approach for fast and secure convolutional neural network predictions},
  author={Li, Shaohua and Xue, Kaiping and Zhu, Bin and Ding, Chenkai and Gao, Xindi and Wei, David and Wan, Tao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8705--8714},
  year={2020}
}
@inproceedings{lee2018single,
  title={Single-image depth estimation based on fourier domain analysis},
  author={Lee, Jae-Han and Heo, Minhyeok and Kim, Kyung-Rae and Kim, Chang-Su},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={330--339},
  year={2018}
}
% FFT
@incollection{nussbaumer1981fast,
  title={The fast Fourier transform},
  author={Nussbaumer, Henri J},
  booktitle={Fast Fourier Transform and Convolution Algorithms},
  pages={80--111},
  year={1981},
  publisher={Springer}
}

%moco v3
@article{Chen2021AnES,
  title={An Empirical Study of Training Self-Supervised Vision Transformers},
  author={Xinlei Chen and Saining Xie and Kaiming He},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={9620-9629}
}

%clip
@inproceedings{Radford2021LearningTV,
  title={Learning Transferable Visual Models From Natural Language Supervision},
  author={Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
  booktitle={International Conference on Machine Learning},
  year={2021}
}

% Lora
@inproceedings{hu2021lora,
  title={LoRA: Low-Rank Adaptation of Large Language Models},
  author={Hu, Edward J and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu and others},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

% Visual Prompts 
@article{bahng2022exploring,
  title={Exploring visual prompts for adapting large-scale models},
  author={Bahng, Hyojin and Jahanian, Ali and Sankaranarayanan, Swami and Isola, Phillip},
  journal={arXiv preprint arXiv:2203.17274},
  volume={1},
  number={3},
  pages={4},
  year={2022}
}

%PETL
@inproceedings{he2022towards,
title={Towards a Unified View of Parameter-Efficient Transfer Learning},
author={Junxian He and Chunting Zhou and Xuezhe Ma and Taylor Berg-Kirkpatrick and Graham Neubig},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=0RDcd5Axok}
}

%V-PETL
@article{yu2022towards,
  title={Towards a Unified View on Visual Parameter-Efficient Transfer Learning},
  author={Yu, Bruce XB and Chang, Jianlong and Liu, Lingbo and Tian, Qi and Chen, Chang Wen},
  journal={arXiv preprint arXiv:2210.00788},
  year={2022}
}


% Fourier
@inproceedings{oppenheim1979phase,
  title={Phase in speech and pictures},
  author={Oppenheim, A and Lim, Jae and Kopec, Gary and Pohlig, SC},
  booktitle={ICASSP'79. IEEE International Conference on Acoustics, Speech, and Signal Processing},
  volume={4},
  pages={632--637},
  year={1979},
  organization={IEEE}
} %可视化相位图
@article{oppenheim1981importance,
  title={The importance of phase in signals},
  author={Oppenheim, Alan V and Lim, Jae S},
  journal={Proceedings of the IEEE},
  volume={69},
  number={5},
  pages={529--541},
  year={1981},
  publisher={IEEE}
}%可视化相位图

@inproceedings{liu2023devil,
  title={The devil is in the frequency: Geminated gestalt autoencoder for self-supervised visual pre-training},
  author={Liu, Hao and Jiang, Xinghua and Li, Xin and Guo, Antai and Hu, Yiqing and Jiang, Deqiang and Ren, Bo},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={2},
  pages={1649--1656},
  year={2023}
}

%inception v1
@inproceedings{szegedy2015going,
  title={Going deeper with convolutions},
  author={Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1--9},
  year={2015}
}

%crossformer
@article{wang2021crossformer,
  title={Crossformer: A versatile vision transformer hinging on cross-scale attention},
  author={Wang, Wenxiao and Yao, Lu and Chen, Long and Lin, Binbin and Cai, Deng and He, Xiaofei and Liu, Wei},
  journal={arXiv preprint arXiv:2108.00154},
  year={2021}
}

%unetr
@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}
}

%VT-Net
@inproceedings{peiris2022robust,
  title={A robust volumetric transformer for accurate 3D tumor segmentation},
  author={Peiris, Himashi and Hayat, Munawar and Chen, Zhaolin and Egan, Gary and Harandi, Mehrtash},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={162--172},
  year={2022},
  organization={Springer}
}

%swinunetr
@inproceedings{tang2022self,
  title={Self-supervised pre-training of swin transformers for 3d medical image analysis},
  author={Tang, Yucheng and Yang, Dong and Li, Wenqi and Roth, Holger R and Landman, Bennett and Xu, Daguang and Nath, Vishwesh and Hatamizadeh, Ali},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20730--20740},
  year={2022}
}
@inproceedings{Hatamizadeh2022SwinUS,
  title={Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images},
  author={Ali Hatamizadeh and V. Nath and Yucheng Tang and Dong Yang and Holger R. Roth and Daguang Xu},
  booktitle={BrainLes@MICCAI},
  year={2022}
}

%D-former
@article{dformer,
  title={D-former: A u-shaped dilated transformer for 3d medical image segmentation},
  author={Wu, Yixuan and Liao, Kuanlun and Chen, Jintai and Wang, Jinhong and Chen, Danny Z and Gao, Honghao and Wu, Jian},
  journal={Neural Computing and Applications},
  pages={1--14},
  year={2022},
  publisher={Springer}
}

%segnext
@article{guo2022segnext,
  title={SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation},
  author={Guo, Meng-Hao and Lu, Cheng-Ze and Hou, Qibin and Liu, Zhengning and Cheng, Ming-Ming and Hu, Shi-Min},
  journal={arXiv preprint arXiv:2209.08575},
  year={2022}
}

% Adapter
@inproceedings{Houlsby2019ParameterEfficientTL,
  title={Parameter-Efficient Transfer Learning for NLP},
  author={Neil Houlsby and Andrei Giurgiu and Stanislaw Jastrzebski and Bruna Morrone and Quentin de Laroussilhe and Andrea Gesmundo and Mona Attariyan and Sylvain Gelly},
  booktitle={ICML},
  year={2019}
}
@article{pfeiffer2020adapterfusion,
  title={AdapterFusion: Non-destructive task composition for transfer learning},
  author={Pfeiffer, Jonas and Kamath, Aishwarya and R{\"u}ckl{\'e}, Andreas and Cho, Kyunghyun and Gurevych, Iryna},
  journal={arXiv preprint arXiv:2005.00247},
  year={2020}
}
@article{pfeiffer2020adapterhub,
  title={Adapterhub: A framework for adapting transformers},
  author={Pfeiffer, Jonas and R{\"u}ckl{\'e}, Andreas and Poth, Clifton and Kamath, Aishwarya and Vuli{\'c}, Ivan and Ruder, Sebastian and Cho, Kyunghyun and Gurevych, Iryna},
  journal={arXiv preprint arXiv:2007.07779},
  year={2020}
}

% residual adapter
@article{rebuffi2017learning,
  title={Learning multiple visual domains with residual adapters},
  author={Rebuffi, Sylvestre-Alvise and Bilen, Hakan and Vedaldi, Andrea},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}
% parcial
@article{yosinski2014transferable,
  title={How transferable are features in deep neural networks?},
  author={Yosinski, Jason and Clune, Jeff and Bengio, Yoshua and Lipson, Hod},
  journal={Advances in neural information processing systems},
  volume={27},
  year={2014}
}
% prompt in nlp
@article{liu2021pre,
  title={Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing},
  author={Liu, Pengfei and Yuan, Weizhe and Fu, Jinlan and Jiang, Zhengbao and Hayashi, Hiroaki and Neubig, Graham},
  journal={arXiv preprint arXiv:2107.13586},
  year={2021}
}
%VPT
@inproceedings{jia2022visual,
  title={Visual prompt tuning},
  author={Jia, Menglin and Tang, Luming and Chen, Bor-Chun and Cardie, Claire and Belongie, Serge and Hariharan, Bharath and Lim, Ser-Nam},
  booktitle={European Conference on Computer Vision},
  pages={709--727},
  year={2022},
  organization={Springer}
}
%Pro-tuning
@article{nie2023pro,
  title={Pro-tuning: Unified prompt tuning for vision tasks},
  author={Nie, Xing and Ni, Bolin and Chang, Jianlong and Meng, Gaofeng and Huo, Chunlei and Xiang, Shiming and Tian, Qi},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2023},
  publisher={IEEE}
}
%AdaptFormer
@article{chen2022adaptformer,
  title={Adaptformer: Adapting vision transformers for scalable visual recognition},
  author={Chen, Shoufa and Ge, Chongjian and Tong, Zhan and Wang, Jiangliu and Song, Yibing and Wang, Jue and Luo, Ping},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={16664--16678},
  year={2022}
}

%ST-Adapter
@article{pan2022st,
  title={St-adapter: Parameter-efficient image-to-video transfer learning},
  author={Pan, Junting and Lin, Ziyi and Zhu, Xiatian and Shao, Jing and Li, Hongsheng},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={26462--26477},
  year={2022}
}

%Beit
@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}
}


@article{xie2022masked,
  title={Masked frequency modeling for self-supervised visual pre-training},
  author={Xie, Jiahao and Li, Wei and Zhan, Xiaohang and Liu, Ziwei and Ong, Yew Soon and Loy, Chen Change},
  journal={arXiv preprint arXiv:2206.07706},
  year={2022}
}

@inproceedings{huang2022attentive,
  title={Attentive Symmetric Autoencoder for Brain MRI Segmentation},
  author={Huang, Junjia and Li, Haofeng and Li, Guanbin and Wan, Xiang},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={203--213},
  year={2022},
  organization={Springer}
}

@article{zhou2022self,
  title={Self pre-training with masked autoencoders for medical image analysis},
  author={Zhou, Lei and Liu, Huidong and Bae, Joseph and He, Junjun and Samaras, Dimitris and Prasanna, Prateek},
  journal={arXiv preprint arXiv:2203.05573},
  year={2022}
}

% ResNet
@inproceedings{he2016deep,
  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}
}
% 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}
}

% FCN
@inproceedings{fcn,
  title={Fully convolutional networks for semantic segmentation},
  author={Long, Jonathan and Shelhamer, Evan and Darrell, Trevor},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={3431--3440},
  year={2015}
}

% U-Net
@inproceedings{unet,
  title={U-net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={International Conference on Medical image computing and computer-assisted intervention},
  pages={234--241},
  year={2015},
  organization={Springer}
}

% 3D U-Net
@inproceedings{3dunet,
  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={International conference on medical image computing and computer-assisted intervention},
  pages={424--432},
  year={2016},
  organization={Springer}
}

% V-Net
@inproceedings{vnet,
  title={V-net: Fully convolutional neural networks for volumetric medical image segmentation},
  author={Milletari, Fausto and Navab, Nassir and Ahmadi, Seyed-Ahmad},
  booktitle={2016 fourth international conference on 3D vision (3DV)},
  pages={565--571},
  year={2016},
  organization={IEEE}
}

% Attention in NLP
@inproceedings{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  booktitle={Advances in neural information processing systems},
  pages={5998--6008},
  year={2017}
}

% Non-Local
@inproceedings{wang2018non,
  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}
}

% U-net++
@incollection{unet++,
  title={Unet++: A nested u-net architecture for medical image segmentation},
  author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},
  booktitle={Deep learning in medical image analysis and multimodal learning for clinical decision support},
  pages={3--11},
  year={2018},
  publisher={Springer}
}

% Unet3+
@inproceedings{huang2020unet,
  title={Unet 3+: A full-scale connected unet for medical image segmentation},
  author={Huang, Huimin and Lin, Lanfen and Tong, Ruofeng and Hu, Hongjie and Zhang, Qiaowei and Iwamoto, Yutaro and Han, Xianhua and Chen, Yen-Wei and Wu, Jian},
  booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1055--1059},
  year={2020},
  organization={IEEE}
}

% ResUNet
@article{zhang2018road,
  title={Road extraction by deep residual u-net},
  author={Zhang, Zhengxin and Liu, Qingjie and Wang, Yunhong},
  journal={IEEE Geoscience and Remote Sensing Letters},
  volume={15},
  number={5},
  pages={749--753},
  year={2018},
  publisher={IEEE}
}

% Rca-u-net
@inproceedings{fang2019rca,
  title={Rca-u-net: Residual channel attention u-net for fast tissue quantification in magnetic resonance fingerprinting},
  author={Fang, Zhenghan and Chen, Yong and Nie, Dong and Lin, Weili and Shen, Dinggang},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={101--109},
  year={2019},
  organization={Springer}
}

% Brainlesion workshop
@inproceedings{li2018automatic,
  title={Automatic brain structures segmentation using deep residual dilated u-net},
  author={Li, Hongwei and Zhygallo, Andrii and Menze, Bjoern},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={385--393},
  year={2018},
  organization={Springer}
}

% Weighted res-unet
@inproceedings{xiao2018weighted,
  title={Weighted res-unet for high-quality retina vessel segmentation},
  author={Xiao, Xiao and Lian, Shen and Luo, Zhiming and Li, Shaozi},
  booktitle={2018 9th international conference on information technology in medicine and education (ITME)},
  pages={327--331},
  year={2018},
  organization={IEEE}
}

% attention NLP
@article{bahdanau2014neural,
  title={Neural machine translation by jointly learning to align and translate},
  author={Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua},
  journal={arXiv preprint arXiv:1409.0473},
  year={2014}
}


% Asymmetric non-local
@inproceedings{zhu2019asymmetric,
  title={Asymmetric non-local neural networks for semantic segmentation},
  author={Zhu, Zhen and Xu, Mengde and Bai, Song and Huang, Tengteng and Bai, Xiang},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={593--602},
  year={2019}
}

@article{schlemper2019attention,
  title={Attention gated networks: Learning to leverage salient regions in medical images},
  author={Schlemper, Jo and Oktay, Ozan and Schaap, Michiel and Heinrich, Mattias and Kainz, Bernhard and Glocker, Ben and Rueckert, Daniel},
  journal={Medical image analysis},
  volume={53},
  pages={197--207},
  year={2019},
  publisher={Elsevier}
}

% DANet
@inproceedings{fu2019dual,
  title={Dual attention network for scene segmentation},
  author={Fu, Jun and Liu, Jing and Tian, Haijie and Li, Yong and Bao, Yongjun and Fang, Zhiwei and Lu, Hanqing},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3146--3154},
  year={2019}
}

%Vit
@inproceedings{dosovitskiy2020vit,
  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={International Conference on Learning Representations},
  year={2021}
}

%deeplabv1v2v3
@article{chen2014semantic,
  title={Semantic image segmentation with deep convolutional nets and fully connected crfs},
  author={Chen, Liang-Chieh and Papandreou, George and Kokkinos, Iasonas and Murphy, Kevin and Yuille, Alan L},
  journal={arXiv preprint arXiv:1412.7062},
  year={2014}
}
@article{chen2017deeplab,
  title={Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs},
  author={Chen, Liang-Chieh and Papandreou, George and Kokkinos, Iasonas and Murphy, Kevin and Yuille, Alan L},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={40},
  number={4},
  pages={834--848},
  year={2017},
  publisher={IEEE}
}
@article{chen2017rethinking,
  title={Rethinking atrous convolution for semantic image segmentation},
  author={Chen, Liang-Chieh and Papandreou, George and Schroff, Florian and Adam, Hartwig},
  journal={arXiv preprint arXiv:1706.05587},
  year={2017}
}

% CCNet
@inproceedings{huang2019ccnet,
  title={Ccnet: Criss-cross attention for semantic segmentation},
  author={Huang, Zilong and Wang, Xinggang and Huang, Lichao and Huang, Chang and Wei, Yunchao and Liu, Wenyu},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={603--612},
  year={2019}
}

% Attention U-Net
@article{oktay2018attention,
  title={Attention u-net: Learning where to look for the pancreas},
  author={Oktay, Ozan and Schlemper, Jo and Folgoc, Loic Le and Lee, Matthew and Heinrich, Mattias and Misawa, Kazunari and Mori, Kensaku and McDonagh, Steven and Hammerla, Nils Y and Kainz, Bernhard and others},
  journal={arXiv preprint arXiv:1804.03999},
  year={2018}
}

% Multi-scale self-guided attention
@article{sinha2020multi,
  title={Multi-scale self-guided attention for medical image segmentation},
  author={Sinha, Ashish and Dolz, Jose},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2020},
  publisher={IEEE}
}

% CPNet
@inproceedings{yu2020context,
  title={Context Prior for Scene Segmentation},
  author={Yu, Changqian and Wang, Jingbo and Gao, Changxin and Yu, Gang and Shen, Chunhua and Sang, Nong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12416--12425},
  year={2020}
}

% DeiT
@article{deit,
  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}},
  journal={arXiv preprint arXiv:2012.12877},
  year={2020}
}

% BERT
@article{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},
  journal={arXiv preprint arXiv:1810.04805},
  year={2018}
}

% DETR
@inproceedings{detr,
  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}
}

% SETR
@inproceedings{zheng2021rethinking,
  title={Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers},
  author={Zheng, Sixiao and Lu, Jiachen and Zhao, Hengshuang and Zhu, Xiatian and Luo, Zekun and Wang, Yabiao and Fu, Yanwei and Feng, Jianfeng and Xiang, Tao and Torr, Philip HS and others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6881--6890},
  year={2021}
}

% tokens to token
@article{t2t,
  title={Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet},
  author={Yuan, Li and Chen, Yunpeng and Wang, Tao and Yu, Weihao and Shi, Yujun and Tay, Francis EH and Feng, Jiashi and Yan, Shuicheng},
  journal={arXiv preprint arXiv:2101.11986},
  year={2021}
}

% BraTS(3 Papers)
@article{menze2014multimodal,
  title={The multimodal brain tumor image segmentation benchmark (BRATS)},
  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 others},
  journal={IEEE transactions on medical imaging},
  volume={34},
  number={10},
  pages={1993--2024},
  year={2014},
  publisher={IEEE}
}

@article{bakas2017advancing,
  title={Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features},
  author={Bakas, Spyridon and Akbari, Hamed and Sotiras, Aristeidis and Bilello, Michel and Rozycki, Martin and Kirby, Justin S and Freymann, John B and Farahani, Keyvan and Davatzikos, Christos},
  journal={Scientific data},
  volume={4},
  pages={170117},
  year={2017},
  publisher={Nature Publishing Group}
}

@article{bakas2018identifying,
  title={Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge},
  author={Bakas, Spyridon and Reyes, Mauricio and Jakab, Andras and Bauer, Stefan and Rempfler, Markus and Crimi, Alessandro and Shinohara, Russell Takeshi and Berger, Christoph and Ha, Sung Min and Rozycki, Martin and others},
  journal={arXiv preprint arXiv:1811.02629},
  year={2018}
}

% KiU-Net
@article{valanarasu2020kiu,
  title={KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation},
  author={Valanarasu, Jeya Maria Jose and Sindagi, Vishwanath A and Hacihaliloglu, Ilker and Patel, Vishal M},
  journal={arXiv preprint arXiv:2010.01663},
  year={2020}
}

% workshop1
@inproceedings{wang20193d,
  title={3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction},
  author={Wang, Feifan and Jiang, Runzhou and Zheng, Liqin and Meng, Chun and Biswal, Bharat},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={131--141},
  year={2019},
  organization={Springer}
}

% workshop2
@inproceedings{li2019multi,
  title={Multi-step Cascaded Networks for Brain Tumor Segmentation},
  author={Li, Xiangyu and Luo, Gongning and Wang, Kuanquan},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={163--173},
  year={2019},
  organization={Springer}
}

%TransUNet
@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}
}

% Transfuse
@article{zhang2021transfuse,
  title={Transfuse: Fusing transformers and cnns for medical image segmentation},
  author={Zhang, Yundong and Liu, Huiye and Hu, Qiang},
  journal={arXiv preprint arXiv:2102.08005},
  year={2021}
}

% MedT
@article{valanarasu2021medical,
  title={Medical transformer: Gated axial-attention for medical image segmentation},
  author={Valanarasu, Jeya Maria Jose and Oza, Poojan and Hacihaliloglu, Ilker and Patel, Vishal M},
  journal={arXiv preprint arXiv:2102.10662},
  year={2021}
}

% MISSFormer
@article{huang2021missformer,
  title={MISSFormer: An Effective Medical Image Segmentation Transformer},
  author={Huang, Xiaohong and Deng, Zhifang and Li, Dandan and Yuan, Xueguang},
  journal={arXiv preprint arXiv:2109.07162},
  year={2021}
}

% TransAttUnet
@article{chen2021transattunet,
  title={TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation},
  author={Chen, Bingzhi and Liu, Yishu and Zhang, Zheng and Lu, Guangming and Zhang, David},
  journal={arXiv preprint arXiv:2107.05274},
  year={2021}
}

% 1st article by R1 during rebuttal
@inproceedings{frey2019memory,
  title={Memory efficient brain tumor segmentation using an autoencoder-regularized U-net},
  author={Frey, Markus and Nau, Matthias},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={388--396},
  year={2019},
  organization={Springer}
}

% 2nd article by R1 during rebuttal
@inproceedings{myronenko2019robust,
  title={Robust semantic segmentation of brain tumor regions from 3D MRIs},
  author={Myronenko, Andriy and Hatamizadeh, Ali},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={82--89},
  year={2019},
  organization={Springer}
}

% Kits2019
@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}
}

% Lits2017
@article{bilic2019liver,
  title={The liver tumor segmentation benchmark (lits). arXiv 2019},
  author={Bilic, P and Christ, PF and Vorontsov, E and Chlebus, G and Chen, H and Dou, Q and Fu, C and Han, X and Heng, P and Hesser, J and others},
  journal={arXiv preprint arXiv:1901.04056},
  year={2019}
}

% TransBTS in MICCAI2021
@inproceedings{wang2021transbts,
  title={Transbts: Multimodal brain tumor segmentation using transformer},
  author={Wang, Wenxuan and Chen, Chen and Ding, Meng and Yu, Hong and Zha, Sen and Li, Jiangyun},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={109--119},
  year={2021},
  organization={Springer}
}

% TransBTSV2
@article{li2022transbtsv2,
  title={TransBTSV2: Wider Instead of Deeper Transformer for Medical Image Segmentation},
  author={Li, Jiangyun and Wang, Wenxuan and Chen, Chen and Zhang, Tianxiang and Zha, Sen and Yu, Hong and Wang, Jing},
  journal={arXiv preprint arXiv:2201.12785},
  year={2022}
}

% segmenter
@article{strudel2021segmenter,
  title={Segmenter: Transformer for Semantic Segmentation},
  author={Strudel, Robin and Garcia, Ricardo and Laptev, Ivan and Schmid, Cordelia},
  journal={arXiv preprint arXiv:2105.05633},
  year={2021}
}

% SegFormer
@article{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},
  journal={arXiv preprint arXiv:2105.15203},
  year={2021}
}

% swin transformer
@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},
  pages={10012--10022},
  year={2021}
}

% PVT
@article{wang2021pyramid,
  title={Pyramid vision transformer: A versatile backbone for dense prediction without convolutions},
  author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
  journal={arXiv preprint arXiv:2102.12122},
  year={2021}
}

% Twins
@inproceedings{chu2021twins,
  title={Twins: Revisiting the design of spatial attention in vision transformers},
  author={Chu, Xiangxiang and Tian, Zhi and Wang, Yuqing and Zhang, Bo and Ren, Haibing and Wei, Xiaolin and Xia, Huaxia and Shen, Chunhua},
  booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
  year={2021}
}

% conformer
@article{peng2021conformer,
  title={Conformer: Local Features Coupling Global Representations for Visual Recognition},
  author={Peng, Zhiliang and Huang, Wei and Gu, Shanzhi and Xie, Lingxi and Wang, Yaowei and Jiao, Jianbin and Ye, Qixiang},
  journal={arXiv preprint arXiv:2105.03889},
  year={2021}
}

% nnunet
@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}
}

% reformer
@article{kitaev2020reformer,
  title={Reformer: The efficient transformer},
  author={Kitaev, Nikita and Kaiser, {\L}ukasz and Levskaya, Anselm},
  journal={arXiv preprint arXiv:2001.04451},
  year={2020}
}

% starformer
@article{guo2019star,
  title={Star-transformer},
  author={Guo, Qipeng and Qiu, Xipeng and Liu, Pengfei and Shao, Yunfan and Xue, Xiangyang and Zhang, Zheng},
  journal={arXiv preprint arXiv:1902.09113},
  year={2019}
}

% longformer
@article{beltagy2020longformer,
  title={Longformer: The long-document transformer},
  author={Beltagy, Iz and Peters, Matthew E and Cohan, Arman},
  journal={arXiv preprint arXiv:2004.05150},
  year={2020}
}

% ETC: Encoding Long and Structured Inputs in Transformers. 
@article{ainslie2020etc,
  title={ETC: Encoding long and structured inputs in transformers},
  author={Ainslie, Joshua and Ontanon, Santiago and Alberti, Chris and Cvicek, Vaclav and Fisher, Zachary and Pham, Philip and Ravula, Anirudh and Sanghai, Sumit and Wang, Qifan and Yang, Li},
  journal={arXiv preprint arXiv:2004.08483},
  year={2020}
}

% Big Bird: Transformers for Longer Sequences
@inproceedings{zaheer2020big,
  title={Big Bird: Transformers for Longer Sequences.},
  author={Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon, Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others},
  booktitle={NeurIPS},
  year={2020}
}

% Seg-Net
@inproceedings{myronenko20183d,
  title={3D MRI brain tumor segmentation using autoencoder regularization},
  author={Myronenko, Andriy},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={311--320},
  year={2018},
  organization={Springer}
}

% An Ensemble of U-Net Models for Kidney Tumor Segmentation with CT images
@article{causey2021ensemble,
  title={An Ensemble of U-Net Models for Kidney Tumor Segmentation with CT images},
  author={Causey, Jason and Stubblefield, Jonathan and Qualls, Jake and Fowler, Jennifer and Cai, Lingrui and Walker, Karl and Guan, Yuanfang and Huang, Xiuzhen},
  journal={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
  year={2021},
  publisher={IEEE}
}

% BiSC-UNet
@inproceedings{wang2019bisc,
  title={BiSC-UNet: A fine segmentation framework for kidney and renal tumor},
  author={Wang, Chuanxia and He, Yuting and Qi, Xiaoming and Zhao, Ziteng and Yang, Guanyu and Zhu, Xiaomei and Zhang, Shaobo and Dillenseger, Jean-Louis and Coatrieux, Jean-Louis},
  booktitle={2019 Kidney Tumor Segmentation Challenge: KiTS19, Miccai 2019},
  year={2019},
  organization={University of Minnesota Libraries Publishing}
}

% SERU: A cascaded SE-ResNeXT U-Net
@article{xie2020seru,
  title={SERU: A cascaded SE-ResNeXT U-Net for kidney and tumor segmentation},
  author={Xie, Xiuzhen and Li, Lei and Lian, Sheng and Chen, Shaohao and Luo, Zhiming},
  journal={Concurrency and Computation: Practice and Experience},
  volume={32},
  number={14},
  pages={e5738},
  year={2020},
  publisher={Wiley Online Library}
}

% A triple-stage self-guided network
@inproceedings{hou2020triple,
  title={A triple-stage self-guided network for kidney tumor segmentation},
  author={Hou, Xiaoshuai and Xie, Chunmei and Li, Fengyi and Wang, Jiaping and Lv, Chuanfeng and Xie, Guotong and Nan, Yang},
  booktitle={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
  pages={341--344},
  year={2020},
  organization={IEEE}
}

% 3nd place on kits19 leaderboard
@misc{mu2019segmentation,
  author={Mu, Guangrui and Lin, Zhiyong and Han, Miaofei and Yao, Guang and Gao, Yaozong},
  title={Segmentation of kidney tumor by multi-resolution VB-nets},
  note={{A}ccessed on: Dec. 10, 2021. [Online]. Available: \url{http://results.kits-challenge.org/miccai2019/manuscripts/gr\_6e.pdf}},
  year={2019},
  publisher={University of Minnesota Libraries Publishing}
}

% 5nd place on kits19 leaderboard
@misc{ma2019solution,
  author={Ma, Jun},
  title={Solution to the kidney tumor segmentation challenge 2019},
  note={{A}ccessed on: Dec. 10, 2021. [Online]. Available: \url{http://results.kits-challenge.org/miccai2019/manuscripts/junma\_1.pdf}},
  year={2019},
  publisher={University of Minnesota Libraries Publishing}
}

% 6nd place on kits19 leaderboard
@misc{liu2019coarse,
  author={Liu, Shuolin},
  title={Coarse to Fine Framework for Kidney Tumor Segmentation},
  note={{A}ccessed on: Dec. 10, 2021. [Online]. Available: \url{http://results.kits-challenge.org/miccai2019/manuscripts/LSL000UD\_3e.pdf}},
  year={2019},
  publisher={University of Minnesota Libraries Publishing}
}

% 7nd place on kits19 leaderboard
@article{zhao2019multi,
  title={Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation},
  author={Zhao, Wenshuai and Zeng, Zengfeng},
  journal={arXiv preprint arXiv:1908.03204},
  year={2019}
}

% 8nd place on kits19 leaderboard
@misc{li2019fully,
  author={Li, Yu},
  title={Fully Automatic Segmentation of Kidney and Tumor Based on Cascaded U-Nets},
  note={{A}ccessed on: Dec. 10, 2021. [Online]. Available: \url{http://results.kits-challenge.org/miccai2019/manuscripts/YuLI\_1e.pdf}},
  year={2019},
  publisher={University of Minnesota Libraries Publishing}
}

% 9nd place on kits19 leaderboard
@article{myronenko20193d,
  title={3d kidneys and kidney tumor semantic segmentation using boundary-aware networks},
  author={Myronenko, Andriy and Hatamizadeh, Ali},
  journal={arXiv preprint arXiv:1909.06684},
  year={2019}
}

% 10nd place on kits19 leaderboard
@misc{chen2019segmentation,
  author={Chen, Ball},
  title={Segmentation of CT Kidney and kidney tumor by MDD-Net},
  note={{A}ccessed on: Dec. 10, 2021. [Online]. Available: \url{http://results.kits-challenge.org/miccai2019/manuscripts/rzchen\_xmu\_5.pdf}},
  year={2019},
  publisher={University of Minnesota Libraries Publishing}
}

% Elehanx-ResNet
@article{han2017automatic,
  title={Automatic liver lesion segmentation using a deep convolutional neural network method},
  author={Han, Xiao},
  journal={arXiv preprint arXiv:1704.07239},
  year={2017}
}

% deepX
@article{yuan2017hierarchical,
  title={Hierarchical convolutional-deconvolutional neural networks for automatic liver and tumor segmentation},
  author={Yuan, Yading},
  journal={arXiv preprint arXiv:1710.04540},
  year={2017}
}

% Medical
@inproceedings{vorontsov2018liver,
  title={Liver lesion segmentation informed by joint liver segmentation},
  author={Vorontsov, Eugene and Tang, An and Pal, Chris and Kadoury, Samuel},
  booktitle={2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},
  pages={1332--1335},
  year={2018},
  organization={IEEE}
}

% Gchlebus-UNet
@article{chlebus2017neural,
  title={Neural network-based automatic liver tumor segmentation with random forest-based candidate filtering},
  author={Chlebus, Grzegorz and Meine, Hans and Moltz, Jan Hendrik and Schenk, Andrea},
  journal={arXiv preprint arXiv:1706.00842},
  year={2017}
}

% Lei
@article{bi2017automatic,
  title={Automatic liver lesion detection using cascaded deep residual networks},
  author={Bi, Lei and Kim, Jinman and Kumar, Ashnil and Feng, Dagan},
  journal={arXiv preprint arXiv:1704.02703},
  year={2017}
}

% I3D
@inproceedings{carreira2017quo,
  title={Quo vadis, action recognition? a new model and the kinetics dataset},
  author={Carreira, Joao and Zisserman, Andrew},
  booktitle={proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={6299--6308},
  year={2017}
}

% H-DenseUNet
@article{li2018h,
  title={H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes},
  author={Li, Xiaomeng and Chen, Hao and Qi, Xiaojuan and Dou, Qi and Fu, Chi-Wing and Heng, Pheng-Ann},
  journal={IEEE transactions on medical imaging},
  volume={37},
  number={12},
  pages={2663--2674},
  year={2018},
  publisher={IEEE}
}


% 下面都是BraTS2020数据集上的结果对比扩充
% MH-UNet
@article{ahmad2021mh,
  title={MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation},
  author={Ahmad, Parvez and Jin, Hai and Alroobaea, Roobaea and Qamar, Saqib and Zheng, Ran and Alnajjar, Fady and Aboudi, Fathia},
  journal={IEEE Access},
  volume={9},
  pages={148384--148408},
  year={2021},
  publisher={IEEE}
}

@inproceedings{vu2020multi,
  title={Multi-decoder networks with multi-denoising inputs for tumor segmentation},
  author={Vu, Minh H and Nyholm, Tufve and L{\"o}fstedt, Tommy},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={412--423},
  year={2020},
  organization={Springer}
}

@article{fidon2020generalized,
  title={Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge},
  author={Fidon, Lucas and Ourselin, S{\'e}bastien and Vercauteren, Tom},
  journal={arXiv preprint arXiv:2011.01614},
  year={2020}
}

@article{ghaffari2020brain,
  title={Brain tumour segmentation using cascaded 3D densely-connected U-net},
  author={Ghaffari, Mina and Sowmya, Arcot and Oliver, Ruth},
  journal={arXiv preprint arXiv:2009.07563},
  year={2020}
}

@article{nguyen2020enhancing,
  title={Enhancing MRI brain tumor segmentation with an additional classification network},
  author={Nguyen, Hieu T and Le, Tung T and Nguyen, Thang V and Nguyen, Nhan T},
  journal={arXiv preprint arXiv:2009.12111},
  year={2020}
}

@inproceedings{liu2020brain,
  title={Brain tumor segmentation network using attention-based fusion and spatial relationship constraint},
  author={Liu, Chenyu and Ding, Wangbin and Li, Lei and Zhang, Zhen and Pei, Chenhao and Huang, Liqin and Zhuang, Xiahai},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={219--229},
  year={2020},
  organization={Springer}
}

% 下面都是BraTS2019数据集上的结果对比扩充
@inproceedings{mckinley2019triplanar,
  title={Triplanar ensemble of 3d-to-2d cnns with label-uncertainty for brain tumor segmentation},
  author={McKinley, Richard and Rebsamen, Michael and Meier, Raphael and Wiest, Roland},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={379--387},
  year={2019},
  organization={Springer}
}

@inproceedings{wang2019automatic,
  title={Automatic brain tumour segmentation and biophysics-guided survival prediction},
  author={Wang, Shuo and Dai, Chengliang and Mo, Yuanhan and Angelini, Elsa and Guo, Yike and Bai, Wenjia},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={61--72},
  year={2019},
  organization={Springer}
}

@inproceedings{chen2019aggregating,
  title={Aggregating multi-scale prediction based on 3D U-Net in brain tumor segmentation},
  author={Chen, Minglin and Wu, Yaozu and Wu, Jianhuang},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={142--152},
  year={2019},
  organization={Springer}
}

@inproceedings{vu2019tunet,
  title={TuNet: End-to-end hierarchical brain tumor segmentation using cascaded networks},
  author={Vu, Minh H and Nyholm, Tufve and L{\"o}fstedt, Tommy},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={174--186},
  year={2019},
  organization={Springer}
}

% CT
@article{nguyen2015segmentation,
  title={Segmentation, separation and pose estimation of prostate brachytherapy seeds in CT images},
  author={Nguyen, Huu-Giao and Fouard, Celine and Troccaz, Jocelyne},
  journal={IEEE Transactions on Biomedical Engineering},
  volume={62},
  number={8},
  pages={2012--2024},
  year={2015},
  publisher={IEEE}
}

%nnFormer
@article{zhou2023nnformer,
  title={nnFormer: volumetric medical image segmentation via a 3D transformer},
  author={Zhou, Hong-Yu and Guo, Jiansen and Zhang, Yinghao and Han, Xiaoguang and Yu, Lequan and Wang, Liansheng and Yu, Yizhou},
  journal={IEEE Transactions on Image Processing},
  year={2023},
  publisher={IEEE}
}

% MRI
@article{huo2017robust,
  title={Robust multicontrast MRI spleen segmentation for splenomegaly using multi-atlas segmentation},
  author={Huo, Yuankai and Liu, Jiaqi and Xu, Zhoubing and Harrigan, Robert L and Assad, Albert and Abramson, Richard G and Landman, Bennett A},
  journal={IEEE Transactions on Biomedical Engineering},
  volume={65},
  number={2},
  pages={336--343},
  year={2017},
  publisher={IEEE}
}

% three traditional methods for medical image segmentation(statistical shape models, contour-based approaches and machine learning-based methods)
@inproceedings{yu2006medical,
  title={Medical images edge detection based on mathematical morphology},
  author={Yu-Qian, Zhao and Wei-Hua, Gui and Zhen-Cheng, Chen and Jing-Tian, Tang and Ling-Yun, Li},
  booktitle={2005 IEEE engineering in medicine and biology 27th annual conference},
  pages={6492--6495},
  year={2006},
  organization={IEEE}
}

@article{tsai2003shape,
  title={A shape-based approach to the segmentation of medical imagery using level sets},
  author={Tsai, Andy and Yezzi, Anthony and Wells, William and Tempany, Clare and Tucker, Dewey and Fan, Ayres and Grimson, W Eric and Willsky, Alan},
  journal={IEEE transactions on medical imaging},
  volume={22},
  number={2},
  pages={137--154},
  year={2003},
  publisher={IEEE}
}

@article{held1997markov,
  title={Markov random field segmentation of brain MR images},
  author={Held, Karsten and Kops, E Rota and Krause, Bernd J and Wells, William M and Kikinis, Ron and Muller-Gartner, H-W},
  journal={IEEE transactions on medical imaging},
  volume={16},
  number={6},
  pages={878--886},
  year={1997},
  publisher={IEEE}
}

% mobilenetv2
@inproceedings{sandler2018mobilenetv2,
  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}
}

% Ghostnet
@inproceedings{han2020ghostnet,
  title={Ghostnet: More features from cheap operations},
  author={Han, Kai and Wang, Yunhe and Tian, Qi and Guo, Jianyuan and Xu, Chunjing and Xu, Chang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1580--1589},
  year={2020}
}

% shufflenetv1
@inproceedings{zhang2018shufflenet,
  title={Shufflenet: An extremely efficient convolutional neural network for mobile devices},
  author={Zhang, Xiangyu and Zhou, Xinyu and Lin, Mengxiao and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={6848--6856},
  year={2018}
}

% shufflenetV2
@inproceedings{ma2018shufflenet,
  title={Shufflenet v2: Practical guidelines for efficient cnn architecture design},
  author={Ma, Ningning and Zhang, Xiangyu and Zheng, Hai-Tao and Sun, Jian},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={116--131},
  year={2018}
}

% MobilenetV1
@article{howard2017mobilenets,
  title={Mobilenets: Efficient convolutional neural networks for mobile vision applications},
  author={Howard, Andrew G and Zhu, Menglong and Chen, Bo and Kalenichenko, Dmitry and Wang, Weijun and Weyand, Tobias and Andreetto, Marco and Adam, Hartwig},
  journal={arXiv preprint arXiv:1704.04861},
  year={2017}
}

% network quantization
@inproceedings{park2017weighted,
  title={Weighted-entropy-based quantization for deep neural networks},
  author={Park, Eunhyeok and Ahn, Junwhan and Yoo, Sungjoo},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5456--5464},
  year={2017}
}

% distilling
@article{hinton2015distilling,
  title={Distilling the knowledge in a neural network},
  author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff and others},
  journal={arXiv preprint arXiv:1503.02531},
  volume={2},
  number={7},
  year={2015}
}

% network pruning
@inproceedings{he2017channel,
  title={Channel pruning for accelerating very deep neural networks},
  author={He, Yihui and Zhang, Xiangyu and Sun, Jian},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={1389--1397},
  year={2017}
}

% slimmable
@article{yu2018slimmable,
  title={Slimmable neural networks},
  author={Yu, Jiahui and Yang, Linjie and Xu, Ning and Yang, Jianchao and Huang, Thomas},
  journal={arXiv preprint arXiv:1812.08928},
  year={2018}
}


% DRNet
@article{zhu2021dynamic,
  title={Dynamic Resolution Network},
  author={Zhu, Mingjian and Han, Kai and Wu, Enhua and Zhang, Qiulin and Nie, Ying and Lan, Zhenzhong and Wang, Yunhe},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

% RANet
@inproceedings{yang2020resolution,
  title={Resolution adaptive networks for efficient inference},
  author={Yang, Le and Han, Yizeng and Chen, Xi and Song, Shiji and Dai, Jifeng and Huang, Gao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2369--2378},
  year={2020}
}

% mutualnet-ECCV2020
@inproceedings{yang2020mutualnet,
  title={Mutualnet: Adaptive convnet via mutual learning from network width and resolution},
  author={Yang, Taojiannan and Zhu, Sijie and Chen, Chen and Yan, Shen and Zhang, Mi and Willis, Andrew},
  booktitle={European conference on computer vision},
  pages={299--315},
  year={2020},
  organization={Springer}
}

% mutualnet-TPAMI
@article{yang2021mutualnet,
  title={MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations},
  author={Yang, Taojiannan and Zhu, Sijie and Mendieta, Matias and Wang, Pu and Balakrishnan, Ravikumar and Lee, Minwoo and Han, Tao and Shah, Mubarak and Chen, Chen},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
  publisher={IEEE}
}

% DVT
@article{wang2021not,
  title={Not all images are worth 16x16 words: Dynamic vision transformers with adaptive sequence length},
  author={Wang, Yulin and Huang, Rui and Song, Shiji and Huang, Zeyi and Huang, Gao},
  journal={arXiv e-prints},
  pages={arXiv--2105},
  year={2021}
}

% blockdrop
@inproceedings{wu2018blockdrop,
  title={Blockdrop: Dynamic inference paths in residual networks},
  author={Wu, Zuxuan and Nagarajan, Tushar and Kumar, Abhishek and Rennie, Steven and Davis, Larry S and Grauman, Kristen and Feris, Rogerio},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={8817--8826},
  year={2018}
}

% getarnet
@inproceedings{chen2019you,
  title={You look twice: Gaternet for dynamic filter selection in cnns},
  author={Chen, Zhourong and Li, Yang and Bengio, Samy and Si, Si},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9172--9180},
  year={2019}
}

% Instanas
@inproceedings{cheng2020instanas,
  title={Instanas: Instance-aware neural architecture search},
  author={Cheng, An-Chieh and Lin, Chieh Hubert and Juan, Da-Cheng and Wei, Wei and Sun, Min},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={04},
  pages={3577--3584},
  year={2020}
}

% VideoIQ
@inproceedings{sun2021dynamic,
  title={Dynamic network quantization for efficient video inference},
  author={Sun, Ximeng and Panda, Rameswar and Chen, Chun-Fu Richard and Oliva, Aude and Feris, Rogerio and Saenko, Kate},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={7375--7385},
  year={2021}
}

%Static lightweight models

% ESPNet
@inproceedings{mehta2018espnet,
  title={Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation},
  author={Mehta, Sachin and Rastegari, Mohammad and Caspi, Anat and Shapiro, Linda and Hajishirzi, Hannaneh},
  booktitle={Proceedings of the european conference on computer vision (ECCV)},
  pages={552--568},
  year={2018}
}

% 3D-ESPNet
@inproceedings{nuechterlein20183d,
  title={3D-ESPNet with pyramidal refinement for volumetric brain tumor image segmentation},
  author={Nuechterlein, Nicholas and Mehta, Sachin},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={245--253},
  year={2018},
  organization={Springer}
}

% S3D-UNet
@inproceedings{chen2018s3d,
  title={S3D-UNet: separable 3D U-Net for brain tumor segmentation},
  author={Chen, Wei and Liu, Boqiang and Peng, Suting and Sun, Jiawei and Qiao, Xu},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={358--368},
  year={2018},
  organization={Springer}
}

% DMFNet
@inproceedings{chen20193d,
  title={3D dilated multi-fiber network for real-time brain tumor segmentation in MRI},
  author={Chen, Chen and Liu, Xiaopeng and Ding, Meng and Zheng, Junfeng and Li, Jiangyun},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={184--192},
  year={2019},
  organization={Springer}
}

% Hdc-net
@article{luo2020hdc,
  title={Hdc-net: Hierarchical decoupled convolution network for brain tumor segmentation},
  author={Luo, Zhengrong and Jia, Zhongdao and Yuan, Zhimin and Peng, Jialin},
  journal={IEEE Journal of Biomedical and Health Informatics},
  volume={25},
  number={3},
  pages={737--745},
  year={2020},
  publisher={IEEE}
}

% BiFPN
@inproceedings{nguyen2020efficient,
  title={Efficient brain tumor segmentation with dilated multi-fiber network and weighted bi-directional feature pyramid network},
  author={Nguyen, Thanh Hau and Le, Cong Hau and Sang, Dinh Viet and Yao, Tingting and Li, Wei and Wang, Zhiyong},
  booktitle={2020 Digital Image Computing: Techniques and Applications (DICTA)},
  pages={1--7},
  year={2020},
  organization={IEEE}
}

% non-local context
@inproceedings{li2020memory,
  title={Memory-efficient automatic kidney and tumor segmentation based on non-local context guided 3D U-Net},
  author={Li, Zhuoying and Pan, Junquan and Wu, Huisi and Wen, Zhenkun and Qin, Jing},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={197--206},
  year={2020},
  organization={Springer}
}

% Efficient medical image segmentation based on knowledge distillation
@article{qin2021efficient,
  title={Efficient medical image segmentation based on knowledge distillation},
  author={Qin, Dian and Bu, Jia-Jun and Liu, Zhe and Shen, Xin and Zhou, Sheng and Gu, Jing-Jun and Wang, Zhi-Hua and Wu, Lei and Dai, Hui-Fen},
  journal={IEEE Transactions on Medical Imaging},
  volume={40},
  number={12},
  pages={3820--3831},
  year={2021},
  publisher={IEEE}
}


% Dynamic Multi-Scale Filters
@inproceedings{he2019dynamic,
  title={Dynamic multi-scale filters for semantic segmentation},
  author={He, Junjun and Deng, Zhongying and Qiao, Yu},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={3562--3572},
  year={2019}
}

% Learning dynamic routing
@inproceedings{li2020learning,
  title={Learning dynamic routing for semantic segmentation},
  author={Li, Yanwei and Song, Lin and Chen, Yukang and Li, Zeming and Zhang, Xiangyu and Wang, Xingang and Sun, Jian},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8553--8562},
  year={2020}
}

% Pixel-wise attentional gating
@inproceedings{kong2019pixel,
  title={Pixel-wise attentional gating for scene parsing},
  author={Kong, Shu and Fowlkes, Charless},
  booktitle={2019 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  pages={1024--1033},
  year={2019},
  organization={IEEE}
}

% channel exchanging
@article{wang2020deep,
  title={Deep multimodal fusion by channel exchanging},
  author={Wang, Yikai and Huang, Wenbing and Sun, Fuchun and Xu, Tingyang and Rong, Yu and Huang, Junzhou},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={4835--4845},
  year={2020}
}

% Swin-UNet
@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}
}

% pytorch
@article{paszke2019pytorch,
  title={Pytorch: An imperative style, high-performance deep learning library},
  author={Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and others},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

% imagenet
@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}
}

% transfer learning for medical image analysis
@article{raghu2019transfusion,
  title={Transfusion: Understanding transfer learning for medical imaging},
  author={Raghu, Maithra and Zhang, Chiyuan and Kleinberg, Jon and Bengio, Samy},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}
@article{morid2021scoping,
  title={A scoping review of transfer learning research on medical image analysis using ImageNet},
  author={Morid, Mohammad Amin and Borjali, Alireza and Del Fiol, Guilherme},
  journal={Computers in biology and medicine},
  volume={128},
  pages={104115},
  year={2021},
  publisher={Elsevier}
}
@article{niu2021distant,
  title={Distant domain transfer learning for medical imaging},
  author={Niu, Shuteng and Liu, Meryl and Liu, Yongxin and Wang, Jian and Song, Houbing},
  journal={IEEE Journal of Biomedical and Health Informatics},
  volume={25},
  number={10},
  pages={3784--3793},
  year={2021},
  publisher={IEEE}
}



% TIP supple

% FTN
@article{he2022fully,
  title={Fully Transformer Network for Skin Lesion Analysis},
  author={He, Xinzi and Tan, Ee-Leng and Bi, Hanwen and Zhang, Xuzhe and Zhao, Shijie and Lei, Baiying},
  journal={Medical Image Analysis},
  pages={102357},
  year={2022},
  publisher={Elsevier}
}

% FAT-Net
@article{wu2022fat,
  title={FAT-Net: Feature adaptive transformers for automated skin lesion segmentation},
  author={Wu, Huisi and Chen, Shihuai and Chen, Guilian and Wang, Wei and Lei, Baiying and Wen, Zhenkun},
  journal={Medical Image Analysis},
  volume={76},
  pages={102327},
  year={2022},
  publisher={Elsevier}
}

% RTNet
@article{huang2022rtnet,
  title={RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation},
  author={Huang, Shiqi and Li, Jianan and Xiao, Yuze and Shen, Ning and Xu, Tingfa},
  journal={IEEE Transactions on Medical Imaging},
  year={2022},
  publisher={IEEE}
}

% frequency related works
@article{si2022inception,
  title={Inception Transformer},
  author={Si, Chenyang and Yu, Weihao and Zhou, Pan and Zhou, Yichen and Wang, Xinchao and Yan, Shuicheng},
  journal={arXiv preprint arXiv:2205.12956},
  year={2022}
}

% frequency related works
@inproceedings{jiang2021focal,
  title={Focal frequency loss for image reconstruction and synthesis},
  author={Jiang, Liming and Dai, Bo and Wu, Wayne and Loy, Chen Change},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={13919--13929},
  year={2021}
}

% frequency related works
@inproceedings{qin2021fcanet,
  title={Fcanet: Frequency channel attention networks},
  author={Qin, Zequn and Zhang, Pengyi and Wu, Fei and Li, Xi},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={783--792},
  year={2021}
}

% frequency related works
@article{zhou2022deep,
  title={Deep Fourier Up-Sampling},
  author={Zhou, Man and Yu, Hu and Huang, Jie and Zhao, Feng and Gu, Jinwei and Loy, Chen Change and Meng, Deyu and Li, Chongyi},
  journal={arXiv preprint arXiv:2210.05171},
  year={2022}
}

% frequency related works
@inproceedings{wang2020high,
  title={High-frequency component helps explain the generalization of convolutional neural networks},
  author={Wang, Haohan and Wu, Xindi and Huang, Zeyi and Xing, Eric P},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8684--8694},
  year={2020}
}


@article{zhou2022self,
  title={Self pre-training with masked autoencoders for medical image analysis},
  author={Zhou, Lei and Liu, Huidong and Bae, Joseph and He, Junjun and Samaras, Dimitris and Prasanna, Prateek},
  journal={arXiv preprint arXiv:2203.05573},
  year={2022}
}

% UPerNet
@inproceedings{xiao2018unified,
  title={Unified perceptual parsing for scene understanding},
  author={Xiao, Tete and Liu, Yingcheng and Zhou, Bolei and Jiang, Yuning and Sun, Jian},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={418--434},
  year={2018}
}

% SAM+PETL
@article{wu2023medical,
  title={Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation},
  author={Wu, Junde and Fu, Rao and Fang, Huihui and Liu, Yuanpei and Wang, Zhaowei and Xu, Yanwu and Jin, Yueming and Arbel, Tal},
  journal={arXiv preprint arXiv:2304.12620},
  year={2023}
}


@article{chai2023ladder,
  title={Ladder Fine-tuning approach for SAM integrating complementary network},
  author={Chai, Shurong and Jain, Rahul Kumar and Teng, Shiyu and Liu, Jiaqing and Li, Yinhao and Tateyama, Tomoko and Chen, Yen-wei},
  journal={arXiv preprint arXiv:2306.12737},
  year={2023}
}

% SAM
@inproceedings{kirillov2023segment,
  title={Segment anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C and Lo, Wan-Yen and others},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4015--4026},
  year={2023}
}