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The Segment Anything Model (SAM) is a recently proposed prompt-based segmentation model in a generic zero-shot segmentation approach.
N. Kumar, R. Verma, S. Sharma, S. Bhargava, A. Vahadane, and A. Sethi, “A dataset and a technique for generalized nuclear segmentation for computational pathology,” IEEE transactions on medical imaging , vol. 36, no. 7, pp. 1550–1560, 2017
2017
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H. Hu, Y. Zheng, Q. Zhou, J. Xiao, S. Chen, and Q. Guan, “Mc-unet: Multi-scale convolution unet for bladder cancer cell segmentation in phase-contrast microscopy images,” in 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) . IEEE, 2019, pp. 1197–1199
2019
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N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International Conference on Machine Learning . PMLR, 2019, pp. 2790–2799
2019
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N. Kumar, R. Verma, D. Anand, Y. Zhou, O. F. Onder, E. Tsougenis, H. Chen, P.-A. Heng, J. Li, Z. Hu et al. , “A multi-organ nucleus segmentation challenge,” IEEE transactions on medical imaging , vol. 39, no. 5, pp. 1380–1391, 2019
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 et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
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Y. Kong, G. Z. Genchev, X. Wang, H. Zhao, and H. Lu, “Nuclear segmentation in histopathological images using two-stage stacked u-nets with attention mechanism,” Frontiers in Bioengineering and Biotechnology , vol. 8, p. 573866, 2020
2020
Earlier work this paper cites.
X. Xie, J. Chen, Y. Li, L. Shen, K. Ma, and Y. Zheng, “Instance-aware self-supervised learning for nuclei segmentation,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V 23 . Springer, 2020, pp. 341–350
2020
Earlier work this paper cites.
M. Sahasrabudhe, S. Christodoulidis, R. Salgado, S. Michiels, S. Loi, F. André, N. Paragios, and M. Vakalopoulou, “Self-supervised nuclei segmentation in histopathological images using attention,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V 23 . Springer, 2020, pp. 393–402
2020
Cited alongside, same era.
Y. Huo, R. Deng, Q. Liu, A. B. Fogo, and H. Yang, “Ai applications in renal pathology,” Kidney international , vol. 99, no. 6, pp. 1309–1320, 2021
2021
Cited alongside, same era.
X. Li, H. Yang, J. He, A. Jha, A. B. Fogo, L. E. Wheless, S. Zhao, and Y. Huo, “Beds: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation,” in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2021, pp. 659–662
2021
Cited alongside, same era.
2023
Closest in time.
J. Ma and B. Wang, “Segment anything in medical images,” arXiv preprint arXiv:2304.12306 , 2023
2023
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2023
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2023
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2021
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2022
Cited alongside, same era.
OpenAI, “Gpt-4 technical report,” 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
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W. Liu, X. Shen, C.-M. Pun, and X. Cun, “Explicit visual prompting for low-level structure segmentations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 19 434–19 445
2023
Closest in time.