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Meta recently released SAM (Segment Anything Model) which is a general-purpose segmentation model.
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J. Silva, A. Histace, O. Romain, X. Dray, and B. Granado, “Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer,” International Journal of Computer Assisted Radiology and Surgery , vol. 9, no. 2, pp. 283–293, 2014
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N. Tajbakhsh, S. R. Gurudu, and J. Liang, “Automated polyp detection in colonoscopy videos using shape and context information,” IEEE Transactions on Medical Imaging , vol. 35, no. 2, pp. 630–644, 2015
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Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmentation,” 2018
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Y. Fang, C. Chen, Y. Yuan, and K.-y. Tong, “Selective feature aggregation network with area-boundary constraints for polyp segmentation,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part I . Berlin, Heidelberg: Springer-Verlag, 2019, p. 302–310. [Online]. Available: https://doi.org/10.1007/978-3-030-32239-7_34
2019
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T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 1877–1901. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
2020
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D. Jha, P. H. Smedsrud, M. A. Riegler, P. Halvorsen, T. de Lange, D. Johansen, and H. D. Johansen, “Kvasir-seg: A segmented polyp dataset,” in International Conference on Multimedia Modeling . Springer, 2020, pp. 451–462
2020
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D.-P. Fan, G.-P. Ji, T. Zhou, G. Chen, H. Fu, J. Shen, and L. Shao, “Pranet: Parallel reverse attention network for polyp segmentation,” 2020
2020
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C.-H. Huang, H.-Y. Wu, and Y.-L. Lin, “Hardnet-mseg: A simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 fps,” 2021
2021
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K. Patel, A. M. Bur, and G. Wang, “Enhanced u-net: A feature enhancement network for polyp segmentation,” 2021
2021
Cited alongside, same era.
J. Wei, Y. Hu, R. Zhang, Z. Li, S. K. Zhou, and S. Cui, “Shallow attention network for polyp segmentation,” 2021
2021
Cited alongside, same era.
X. Zhao, L. Zhang, and H. Lu, “Automatic polyp segmentation via multi-scale subtraction network,” in International Conference on Medical Image Computing and Computer-Assisted Intervention , 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:236976302
2021
Cited alongside, same era.
Y. Sun, G. Chen, T. Zhou, Y. Zhang, and N. Liu, “Context-aware cross-level fusion network for camouflaged object detection,” 2021
2021
Cited alongside, same era.
Z. Yin, K. Liang, Z. Ma, and J. Guo, “Duplex contextual relation network for polyp segmentation,” 2022
2022
J. Li, J. Jain, and H. Shi, “Matting anything,” 2023
2023
Closest in time.
R. Deng, C. Cui, Q. Liu, T. Yao, L. W. Remedios, S. Bao, B. A. Landman, L. E. Wheless, L. A. Coburn, K. T. Wilson, Y. Wang, S. Zhao, A. B. Fogo, H. Yang, Y. Tang, and Y. Huo, “Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging,” 2023
2023
Closest in time.
J. Ma, Y. He, F. Li, L. Han, C. You, and B. Wang, “Segment anything in medical images,” 2023
2023
Closest in time.
T. Zhou, Y. Zhang, Y. Zhou, Y. Wu, and C. Gong, “Can sam segment polyps?” 2023
2023
Closest in time.
T. Chen, L. Zhu, C. Ding, R. Cao, Y. Wang, Z. Li, L. Sun, P. Mao, and Y. Zang, “Sam fails to segment anything? – sam-adapter: Adapting sam in underperformed scenes: Camouflage, shadow, medical image segmentation, and more,” 2023
2023
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Cited alongside, same era.
T. Zhou, Y. Zhou, C. Gong, J. Yang, and Y. Zhang, “Feature aggregation and propagation network for camouflaged object detection,” IEEE Transactions on Image Processing , vol. 31, pp. 7036–7047, 2022. [Online]. Available: https://doi.org/10.1109%2Ftip.2022.3217695
2022
Cited alongside, same era.
W. Zhang, C. Fu, Y. Zheng, F. Zhang, Y. Zhao, and C.-W. Sham, “Hsnet: A hybrid semantic network for polyp segmentation,” Computers in Biology and Medicine , vol. 150, p. 106173, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0010482522008812
2022
Cited alongside, same era.
2023
Cited alongside, same era.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, and R. Girshick, “Segment anything,” 2023
2023
Cited alongside, same era.
T. Yu, R. Feng, R. Feng, J. Liu, X. Jin, W. Zeng, and Z. Chen, “Inpaint anything: Segment anything meets image inpainting,” 2023
2023
Cited alongside, same era.
L. Tang, H. Xiao, and B. Li, “Can sam segment anything? when sam meets camouflaged object detection,” 2023
2023
Cited alongside, same era.
J. Yao, X. Wang, L. Ye, and W. Liu, “Matte anything: Interactive natural image matting with segment anything models,” 2023
2023
Cited alongside, same era.
Closest in time.
Y. Li, M. Hu, and X. Yang, “Polyp-sam: Transfer sam for polyp segmentation,” 2023
2023
Closest in time.
S. Roy, T. Wald, G. Koehler, M. R. Rokuss, N. Disch, J. Holzschuh, D. Zimmerer, and K. H. Maier-Hein, “Sam.md: Zero-shot medical image segmentation capabilities of the segment anything model,” 2023
2023
Closest in time.
S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu, and L. Zhang, “Grounding dino: Marrying dino with grounded pre-training for open-set object detection,” 2023
2023
Closest in time.
R. Zhang, G. Li, Z. Li, S. Cui, D. Qian, and Y. Yu, “Adaptive context selection for polyp segmentation,” 2023
2023
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R. Zhang, P. Lai, X. Wan, D.-J. Fan, F. Gao, X.-J. Wu, and G. Li, “Lesion-aware dynamic kernel for polyp segmentation,” 2023
2023
Closest in time.
T. Zhou, Y. Zhou, K. He, C. Gong, J. Yang, H. Fu, and D. Shen, “Cross-level feature aggregation network for polyp segmentation,” Pattern Recognition , vol. 140, p. 109555, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0031320323002558
2023
Closest in time.
B. Dong, W. Wang, D.-P. Fan, J. Li, H. Fu, and L. Shao, “Polyp-pvt: Polyp segmentation with pyramid vision transformers,” 2023
2023
Closest in time.