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Segment Anything Model (SAM), a prompt-driven foundation model for natural image segmentation, has demonstrated impressive zero-shot performance.
Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge
B Landman, Z Xu, J Eugenio Igelsias, M Styner, T Langerak, and A Klein · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac mri
MR Avendi, Arash Kheradvar, and Hamid Jafarkhani · 2016
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Deep learning for brain mri segmentation: state of the art and future directions
Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi, Daniel L Rubin, and Bradley J Erickson · 2017
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Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved?
Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, et al · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Automated design of deep learning methods for biomedical image segmentation
Fabian Isensee, Paul F Jäger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein · 2019
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Generalized intersection over union: A metric and a loss for bounding box regression
Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese · 2019
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Swin-unet: Unet-like pure transformer for medical image segmentation
Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang · 2021
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Transunet: Transformers make strong encoders for medical image segmentation
Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L Yuille, and Yuyin Zhou · 2021
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Per-pixel classification is not all you need for semantic segmentation
Bowen Cheng, Alex Schwing, and Alexander Kirillov · 2021
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Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger R Roth, and Daguang Xu · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Transbts: Multimodal brain tumor segmentation using transformer
Wenxuan Wang, Chen Chen, Meng Ding, Hong Yu, Sen Zha, and Jiangyun Li · 2021
Cited alongside, same era.
nnformer: Interleaved transformer for volumetric segmentation
Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, and Yizhou Yu · 2021
Cited alongside, same era.
Unetr: Transformers for 3d medical image segmentation
Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger R Roth, and Daguang Xu · 2022
Cited alongside, same era.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
Cited alongside, same era.
S Mohapatra, A Gosai, and G Schlaug · 2023
Later among the works it cites.
Gpt-4 technical report. arxiv 2303.08774
R OpenAI · 2023
Later among the works it cites.
Sam. md: Zero-shot medical image segmentation capabilities of the segment anything model
Saikat Roy, Tassilo Wald, Gregor Koehler, Maximilian R Rokuss, Nico Disch, Julius Holzschuh, David Zimmerer, and Klaus H Maier-Hein · 2023
Later among the works it cites.
Autosam: Adapting sam to medical images by overloading the prompt encoder
Tal Shaharabany, Aviad Dahan, Raja Giryes, and Lior Wolf · 2023
Later among the works it cites.
Sam. md: Zero-shot medical image segmentation capabilities of the segment anything model
Tassilo Wald, Saikat Roy, Gregor Koehler, Nico Disch, Maximilian Rouven Rokuss, Julius Holzschuh, David Zimmerer, and Klaus Maier-Hein · 2023
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Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
Yuanfeng Ji, Haotian Bai, Chongjian Ge, Jie Yang, Ye Zhu, Ruimao Zhang, Zhen Li, Lingyan Zhanng, Wanling Ma, Xiang Wan, et al · 2022
Cited alongside, same era.
Sam3d: Segment anything model in volumetric medical images
Nhat-Tan Bui, Dinh-Hieu Hoang, Minh-Triet Tran, and Ngan Le · 2023
Cited alongside, same era.
Desam: Decoupling segment anything model for generalizable medical image segmentation
Yifan Gao, Wei Xia, Dingdu Hu, and Xin Gao · 2023
Cited alongside, same era.
3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable medical image segmentation
Shizhan Gong, Yuan Zhong, Wenao Ma, Jinpeng Li, Zhao Wang, Jingyang Zhang, Pheng-Ann Heng, and Qi Dou · 2023
Cited alongside, same era.
Accuracy of segment-anything model (sam) in medical image segmentation tasks
Sheng He, Rina Bao, Jingpeng Li, P Ellen Grant, and Yangming Ou · 2023
Cited alongside, same era.
Chuanfei Hu and Xinde Li · 2023
Cited alongside, same era.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
Cited alongside, same era.
Later among the works it cites.
Sam meets robotic surgery: An empirical study in robustness perspective
An Wang, Mobarakol Islam, Mengya Xu, Yang Zhang, and Hongliang Ren · 2023
Later among the works it cites.
Medical sam adapter: Adapting segment anything model for medical image segmentation
Junde Wu, Rao Fu, Huihui Fang, Yuanpei Liu, Zhaowei Wang, Yanwu Xu, Yueming Jin, and Tal Arbel · 2023
Later among the works it cites.
Customized segment anything model for medical image segmentation
Kaidong Zhang and Dong Liu · 2023
Later among the works it cites.
Tao Zhou, Yizhe Zhang, Yi Zhou, Ye Wu, and Chen Gong · 2023
Later among the works it cites.
Sam3d: Segment anything model in volumetric medical images
Nhat-Tan Bui, Dinh-Hieu Hoang, Minh-Triet Tran, Gianfranco Doretto, Donald Adjeroh, Brijesh Patel, Arabinda Choudhary, and Ngan Le · 2024
Closest in time.
Polyp-sam: Transfer sam for polyp segmentation
Yuheng Li, Mingzhe Hu, and Xiaofeng Yang · 2024
Closest in time.
Segment anything in medical images
Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang · 2024
Closest in time.
Sam 2: Segment anything in images and videos
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al · 2024
Closest in time.
Segment anything model for medical image segmentation: Current applications and future directions
Yichi Zhang, Zhenrong Shen, and Rushi Jiao · 2024
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