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The Segment Anything Model (SAM) exhibits promise in generic object segmentation and offers potential for various applications.
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR (2020)
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González, C., Bravo-Sánchez, L., Arbelaez, P.: ISINet: An instance-based approach for surgical instrument segmentation. In: MICCAI. pp. 595–605. Springer (2020)
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Ni, Z.L., Bian, G.B., Wang, G.A., Zhou, X.H., Hou, Z.G., Chen, H.B., Xie, X.L.: Pyramid attention aggregation network for semantic segmentation of surgical instruments. In: AAAI. vol. 34, pp. 11782–11790 (2020)
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Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: MICCAI. pp. 679–689. Springer (2020)
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Ding, H., Liu, C., Wang, S., Jiang, X.: Vision-language transformer and query generation for referring segmentation. In: ICCV. pp. 16321–16330 (2021)
2021
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Feng, G., Hu, Z., Zhang, L., Lu, H.: Encoder fusion network with co-attention embedding for referring image segmentation. In: CVPR. pp. 15506–15515 (2021)
2021
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Liu, D., Li, Q., Jiang, T., Wang, Y., Miao, R., Shan, F., Li, Z.: Towards unified surgical skill assessment. In: CVPR. pp. 9522–9531 (2021)
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: ICML. pp. 8748–8763. PMLR (2021)
Chen, T., Zhu, L., Deng, C., Cao, R., Wang, Y., Zhang, S., Li, Z., Sun, L., Zang, Y., Mao, P.: SAM-Adapter: Adapting segment anything in underperformed scenes. In: ICCV Workshop. pp. 3367–3375 (2023)
2023
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2023
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Deng, R., Cui, C., Liu, Q., Yao, T., Remedios, L.W., Bao, S., Landman, B.A., Tang, Y., Wheless, L.E., Coburn, L.A., et al.: Segment anything model (SAM) for digital pathology: Assess zero-shot segmentation on whole slide imaging. In: Medical Imaging with Deep Learning, Short Paper Track (2023)
2023
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2023
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2021
Cited alongside, same era.
Azofeifa, J.D., Noguez, J., Ruiz, S., Molina-Espinosa, J.M., Magana, A.J., Benes, B.: Systematic review of multimodal human–computer interaction. In: Informatics. vol. 9, p. 13. MDPI (2022)
2022
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Birlo, M., Edwards, P.E., Clarkson, M., Stoyanov, D.: Utility of optical see-through head mounted displays in augmented reality-assisted surgery: A systematic review. Medical Image Analysis 77
2022
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Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: CVPR. pp. 1290–1299 (2022)
2022
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Kim, N., Kim, D., Lan, C., Zeng, W., Kwak, S.: ReSTR: Convolution-free referring image segmentation using transformers. In: CVPR. pp. 18145–18154 (2022)
2022
Cited alongside, same era.
Lüddecke, T., Ecker, A.: Image segmentation using text and image prompts. In: CVPR. pp. 7086–7096 (2022)
2022
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Wang, Z., Lu, Y., Li, Q., Tao, X., Guo, Y., Gong, M., Liu, T.: CRIS: CLIP-driven referring image segmentation. In: CVPR. pp. 11686–11695 (2022)
2022
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2022
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Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., Dollar, P., Girshick, R.: Segment anything. In: ICCV. pp. 4015–4026 (2023)
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Mazurowski, M.A., Dong, H., Gu, H., Yang, J., Konz, N., Zhang, Y.: Segment anything model for medical image analysis: An experimental study. Medical Image Analysis p. 102918 (2023)
2023
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Wald, T., Roy, S., Koehler, G., Disch, N., Rokuss, M.R., Holzschuh, J., Zimmerer, D., Maier-Hein, K.: SAM. MD: Zero-shot medical image segmentation capabilities of the segment anything model. In: Medical Imaging with Deep Learning, Short Paper Track (2023)
2023
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Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: SAM meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: MICCAI Workshop. pp. 234–244 (2023)
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Zhou, Z., Alabi, O., Wei, M., Vercauteren, T., Shi, M.: Text promptable surgical instrument segmentation with vision-language models. In: NeurIPS (2023)
2023
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Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., et al.: Segment anything model for medical images? Medical Image Analysis 92
2024
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Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nature Communications 15
2024
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Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: SurgicalSAM: Efficient class promptable surgical instrument segmentation. In: AAAI (2024)
2024
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Zhang, R., Jiang, Z., Guo, Z., Yan, S., Pan, J., Dong, H., Qiao, Y., Gao, P., Li, H.: Personalize segment anything model with one shot. In: ICLR (2024)
2024
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