@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@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(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@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})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@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(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})
@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}
}
@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}
}
@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{Authors14,
 author = {FirstName LastName},
 title = {The frobnicatable foo filter},
 note = {Face and Gesture submission ID 324. Supplied as supplemental material {\tt fg324.pdf}},
 year = 2014
}

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

@article{Alpher02,
author = {FirstName Alpher},
title = {Frobnication},
journal = PAMI,
volume = 12,
number = 1,
pages = {234--778},
year = 2002
}

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

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

@inproceedings{Alpher05,
author = {FirstName Alpher and FirstName Gamow},
title = {Can a computer frobnicate?},
booktitle = CVPR,
pages = {234--778},
year = 2005
}

@article{vits,
  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}
}

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

@article{medsam,
  title={Segment anything in medical images},
  author={Ma, Jun and Wang, Bo},
  journal={arXiv preprint arXiv:2304.12306},
  year={2023}
}

@article{msa,
  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{sammed2d,
  title={SAM-Med2D},
  author={Cheng, Junlong and Ye, Jin and Deng, Zhongying and Chen, Jianpin and Li, Tianbin and Wang, Haoyu and Su, Yanzhou and Huang, Ziyan and Chen, Jilong and Jiang, Lei and others},
  journal={arXiv preprint arXiv:2308.16184},
  year={2023}
}

@article{medlsam,
  title={MedLSAM: Localize and Segment Anything Model for 3D Medical Images},
  author={Lei, Wenhui and Wei, Xu and Zhang, Xiaofan and Li, Kang and Zhang, Shaoting},
  journal={arXiv preprint arXiv:2306.14752},
  year={2023}
}

@article{sam3d,
  title={SAM3D: Segment Anything Model in Volumetric Medical Images},
  author={Bui, Nhat-Tan and Hoang, Dinh-Hieu and Tran, Minh-Triet and Le, Ngan},
  journal={arXiv preprint arXiv:2309.03493},
  year={2023}
}

@article{sam3dadapter,
  title={3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Promptable Medical Image Segmentation},
  author={Gong, Shizhan and Zhong, Yuan and Ma, Wenao and Li, Jinpeng and Wang, Zhao and Zhang, Jingyang and Heng, Pheng-Ann and Dou, Qi},
  journal={arXiv preprint arXiv:2306.13465},
  year={2023}
}

@article{sammad3d,
  title={SAM-Med3D},
  author={Wang, Haoyu and Guo, Sizheng and Ye, Jin and Deng, Zhongying and Cheng, Junlong and Li, Tianbin and Chen, Jianpin and Su, Yanzhou and Huang, Ziyan and Shen, Yiqing and others},
  journal={arXiv preprint arXiv:2310.15161},
  year={2023}
}

@article{DINO,
  title={Dino: Detr with improved denoising anchor boxes for end-to-end object detection},
  author={Zhang, Hao and Li, Feng and Liu, Shilong and Zhang, Lei and Su, Hang and Zhu, Jun and Ni, Lionel M and Shum, Heung-Yeung},
  journal={arXiv preprint arXiv:2203.03605},
  year={2022}
}

@article{groundingDINO,
  title={Grounding dino: Marrying dino with grounded pre-training for open-set object detection},
  author={Liu, Shilong and Zeng, Zhaoyang and Ren, Tianhe and Li, Feng and Zhang, Hao and Yang, Jie and Li, Chunyuan and Yang, Jianwei and Su, Hang and Zhu, Jun and others},
  journal={arXiv preprint arXiv:2303.05499},
  year={2023}
}

@article{seggpt,
  title={Seggpt: Segmenting everything in context},
  author={Wang, Xinlong and Zhang, Xiaosong and Cao, Yue and Wang, Wen and Shen, Chunhua and Huang, Tiejun},
  journal={arXiv preprint arXiv:2304.03284},
  year={2023}
}

@article{sam,
  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},
  journal={arXiv preprint arXiv:2304.02643},
  year={2023}
}

@article{trackAnything,
  title={Track anything: Segment anything meets videos},
  author={Yang, Jinyu and Gao, Mingqi and Li, Zhe and Gao, Shang and Wang, Fangjing and Zheng, Feng},
  journal={arXiv preprint arXiv:2304.11968},
  year={2023}
}

@article{text2seg,
  title={Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models},
  author={Zhang, Jielu and Zhou, Zhongliang and Mai, Gengchen and Mu, Lan and Hu, Mengxuan and Li, Sheng},
  journal={arXiv preprint arXiv:2304.10597},
  year={2023}
}

@article{zero1,
  title={Segment anything model for medical image analysis: an experimental study},
  author={Mazurowski, Maciej A and Dong, Haoyu and Gu, Hanxue and Yang, Jichen and Konz, Nicholas and Zhang, Yixin},
  journal={Medical Image Analysis},
  volume={89},
  pages={102918},
  year={2023},
  publisher={Elsevier}
}

@article{zero2,
  title={Segment anything model for medical images?},
  author={Huang, Yuhao and Yang, Xin and Liu, Lian and Zhou, Han and Chang, Ao and Zhou, Xinrui and Chen, Rusi and Yu, Junxuan and Chen, Jiongquan and Chen, Chaoyu and others},
  journal={arXiv preprint arXiv:2304.14660},
  year={2023}
}

@article{zero3,
  title={Accuracy of segment-anything model (sam) in medical image segmentation tasks},
  author={He, Sheng and Bao, Rina and Li, Jingpeng and Grant, P Ellen and Ou, Yangming},
  journal={arXiv preprint arXiv:2304.09324},
  year={2023}
}

@article{gap1,
  title={Segment anything model for medical images?},
  author={Huang, Yuhao and Yang, Xin and Liu, Lian and Zhou, Han and Chang, Ao and Zhou, Xinrui and Chen, Rusi and Yu, Junxuan and Chen, Jiongquan and Chen, Chaoyu and others},
  journal={arXiv preprint arXiv:2304.14660},
  year={2023}
}

@article{Abdomenct1k,
  title={Abdomenct-1k: Is abdominal organ segmentation a solved problem?},
  author={Ma, Jun and Zhang, Yao and Gu, Song and Zhu, Cheng and Ge, Cheng and Zhang, Yichi and An, Xingle and Wang, Congcong and Wang, Qiyuan and Liu, Xin and others},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={44},
  number={10},
  pages={6695--6714},
  year={2021},
  publisher={IEEE}
}

@article{CTPelvic1K,
  title={Deep learning to segment pelvic bones: large-scale CT datasets and baseline models},
  author={Liu, Pengbo and Han, Hu and Du, Yuanqi and Zhu, Heqin and Li, Yinhao and Gu, Feng and Xiao, Honghu and Li, Jun and Zhao, Chunpeng and Xiao, Li and others},
  journal={International Journal of Computer Assisted Radiology and Surgery},
  volume={16},
  pages={749--756},
  year={2021},
  publisher={Springer}
}
@article{word,
  title={Word: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from ct image},
  author={Luo, Xiangde and Liao, Wenjun and Xiao, Jianghong and Chen, Jieneng and Song, Tao and Zhang, Xiaofan and Li, Kang and Metaxas, Dimitris N and Wang, Guotai and Zhang, Shaoting},
  journal={arXiv preprint arXiv:2111.02403},
  year={2021}
}
@article{wasserthal2023totalsegmentator,
  title={Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images},
  author={Wasserthal, Jakob and Breit, Hanns-Christian and Meyer, Manfred T and Pradella, Maurice and Hinck, Daniel and Sauter, Alexander W and Heye, Tobias and Boll, Daniel T and Cyriac, Joshy and Yang, Shan and others},
  journal={Radiology: Artificial Intelligence},
  volume={5},
  number={5},
  year={2023},
  publisher={Radiological Society of North America}
}
@article{lora,
  title={Lora: Low-rank adaptation of large language models},
  author={Hu, Edward J and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu},
  journal={arXiv preprint arXiv:2106.09685},
  year={2021}
}

@article{chaos,
  title={CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation},
  author={Kavur, A Emre and Gezer, N Sinem and Bar{\i}{\c{s}}, Mustafa and Aslan, Sinem and Conze, Pierre-Henri and Groza, Vladimir and Pham, Duc Duy and Chatterjee, Soumick and Ernst, Philipp and {\"O}zkan, Sava{\c{s}} and others},
  journal={Medical Image Analysis},
  volume={69},
  pages={101950},
  year={2021},
  publisher={Elsevier}
}

@inproceedings{landman2015miccai,
  title={Miccai multi-atlas labeling beyond the cranial vault--workshop and challenge},
  author={Landman, Bennett and Xu, Zhoubing and Igelsias, J and Styner, Martin and Langerak, T and Klein, Arno},
  booktitle={Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge},
  volume={5},
  pages={12},
  year={2015}
}

@inproceedings{unet,
  title={U-net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18},
  pages={234--241},
  year={2015},
  organization={Springer}
}

@article{nnunet,
  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}
}

@inproceedings{SSLALP,
  title={Self-supervision with superpixels: Training few-shot medical image segmentation without annotation},
  author={Ouyang, Cheng and Biffi, Carlo and Chen, Chen and Kart, Turkay and Qiu, Huaqi and Rueckert, Daniel},
  booktitle={Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XXIX 16},
  pages={762--780},
  year={2020},
  organization={Springer}
}

@article{kavur2021chaos,
  title={CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation},
  author={Kavur, A Emre and Gezer, N Sinem and Bar{\i}{\c{s}}, Mustafa and Aslan, Sinem and Conze, Pierre-Henri and Groza, Vladimir and Pham, Duc Duy and Chatterjee, Soumick and Ernst, Philipp and {\"O}zkan, Sava{\c{s}} and others},
  journal={Medical Image Analysis},
  volume={69},
  pages={101950},
  year={2021},
  publisher={Elsevier}
}

@inproceedings{quan2022images,
  title={Which images to label for few-shot medical landmark detection?},
  author={Quan, Quan and Yao, Qingsong and Li, Jun and Zhou, S Kevin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20606--20616},
  year={2022}
}
@inproceedings{yao2021one,
  title={One-shot medical landmark detection},
  author={Yao, Qingsong and Quan, Quan and Xiao, Li and Kevin Zhou, S},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proceedings, Part II 24},
  pages={177--188},
  year={2021},
  organization={Springer}
}
@article{snell2017prototypical,
  title={Prototypical networks for few-shot learning},
  author={Snell, Jake and Swersky, Kevin and Zemel, Richard},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}
@inproceedings{sung2018learning,
  title={Learning to compare: Relation network for few-shot learning},
  author={Sung, Flood and Yang, Yongxin and Zhang, Li and Xiang, Tao and Torr, Philip HS and Hospedales, Timothy M},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1199--1208},
  year={2018}
}
@article{garcia2017few,
  title={Few-shot learning with graph neural networks},
  author={Garcia, Victor and Bruna, Joan},
  journal={arXiv preprint arXiv:1711.04043},
  year={2017}
}
@article{vinyals2016matching,
  title={Matching networks for one shot learning},
  author={Vinyals, Oriol and Blundell, Charles and Lillicrap, Timothy and Wierstra, Daan and others},
  journal={Advances in neural information processing systems},
  volume={29},
  year={2016}
}
@article{fei2006one,
  title={One-shot learning of object categories},
  author={Fei-Fei, Li and Fergus, Robert and Perona, Pietro},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={28},
  number={4},
  pages={594--611},
  year={2006},
  publisher={IEEE}
}
@inproceedings{lake2011one,
  title={One shot learning of simple visual concepts},
  author={Lake, Brenden and Salakhutdinov, Ruslan and Gross, Jason and Tenenbaum, Joshua},
  booktitle={Proceedings of the annual meeting of the cognitive science society},
  volume={33},
  number={33},
  year={2011}
}
% Few-shot segment
@inproceedings{zhang2019pyramid,
  title={Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation},
  author={Zhang, Chi and Lin, Guosheng and Liu, Fayao and Guo, Jiushuang and Wu, Qingyao and Yao, Rui},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={9587--9595},
  year={2019}
}
@article{siam2020weakly,
  title={Weakly supervised few-shot object segmentation using co-attention with visual and semantic embeddings},
  author={Siam, Mennatullah and Doraiswamy, Naren and Oreshkin, Boris N and Yao, Hengshuai and Jagersand, Martin},
  journal={arXiv preprint arXiv:2001.09540},
  year={2020}
}
@inproceedings{zhang2019canet,
  title={Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning},
  author={Zhang, Chi and Lin, Guosheng and Liu, Fayao and Yao, Rui and Shen, Chunhua},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5217--5226},
  year={2019}
}
@article{roy2020squeeze,
  title={‘Squeeze \& excite’guided few-shot segmentation of volumetric images},
  author={Roy, Abhijit Guha and Siddiqui, Shayan and P{\"o}lsterl, Sebastian and Navab, Nassir and Wachinger, Christian},
  journal={Medical image analysis},
  volume={59},
  pages={101587},
  year={2020},
  publisher={Elsevier}
}
@inproceedings{tian2020differentiable,
  title={Differentiable meta-learning model for few-shot semantic segmentation},
  author={Tian, Pinzhuo and Wu, Zhangkai and Qi, Lei and Wang, Lei and Shi, Yinghuan and Gao, Yang},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={07},
  pages={12087--12094},
  year={2020}
}
@article{hendryx2019meta,
  title={Meta-learning initializations for image segmentation},
  author={Hendryx, Sean M and Leach, Andrew B and Hein, Paul D and Morrison, Clayton T},
  journal={arXiv preprint arXiv:1912.06290},
  year={2019}
}
@article{shaban2017one,
  title={One-shot learning for semantic segmentation},
  author={Shaban, Amirreza and Bansal, Shray and Liu, Zhen and Essa, Irfan and Boots, Byron},
  journal={arXiv preprint arXiv:1709.03410},
  year={2017}
}
@inproceedings{dong2018few,
  title={Few-shot semantic segmentation with prototype learning.},
  author={Dong, Nanqing and Xing, Eric P},
  booktitle={BMVC},
  volume={3},
  number={4},
  year={2018}
}
@inproceedings{siam2019amp,
  title={Amp: Adaptive masked proxies for few-shot segmentation},
  author={Siam, Mennatullah and Oreshkin, Boris N and Jagersand, Martin},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={5249--5258},
  year={2019}
}
@article{zhang2020sg,
  title={Sg-one: Similarity guidance network for one-shot semantic segmentation},
  author={Zhang, Xiaolin and Wei, Yunchao and Yang, Yi and Huang, Thomas S},
  journal={IEEE transactions on cybernetics},
  volume={50},
  number={9},
  pages={3855--3865},
  year={2020},
  publisher={IEEE}
}
@inproceedings{wang2022few,
  title={Few-shot Medical Image Segmentation Regularized with Self-reference and Contrastive Learning},
  author={Wang, Runze and Zhou, Qin and Zheng, Guoyan},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2022: 25th International Conference, Singapore, September 18--22, 2022, Proceedings, Part IV},
  pages={514--523},
  year={2022},
  organization={Springer}
}
@article{samed,
  title={Customized segment anything model for medical image segmentation},
  author={Zhang, Kaidong and Liu, Dong},
  journal={arXiv preprint arXiv:2304.13785},
  year={2023}
}
@inproceedings{wang2019panet,
  title={Panet: Few-shot image semantic segmentation with prototype alignment},
  author={Wang, Kaixin and Liew, Jun Hao and Zou, Yingtian and Zhou, Daquan and Feng, Jiashi},
  booktitle={proceedings of the IEEE/CVF international conference on computer vision},
  pages={9197--9206},
  year={2019}
}