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The Segment Anything Model (SAM) is a powerful vision foundation model that is revolutionizing the traditional paradigm of segmentation.
Mannor, S., Jin, X., Han, J. and Zhang, X.: K-medoids clustering. Encyclopedia of machine learning. (2011)
2011
Earlier work this paper cites.
Milletari, F., Navab, N. and Ahmadi, S.A.: V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In: Fourth International Conference on 3D Vision (3DV) (2016)
2016
Earlier work this paper cites.
Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M. and Sorkine-Hornung, A.: A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
2016
Earlier work this paper cites.
Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M. and Padoy, N.: Endonet: a deep architecture for recognition tasks on laparoscopic videos. IEEE transactions on medical imaging, 36(1), pp.86-97. (2016)
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Kalia, M., Mathur, P., Tsang, K., Black, P., Navab, N. and Salcudean, S.: Evaluation of a marker-less, intra-operative, augmented reality guidance system for robot-assisted laparoscopic radical prostatectomy. International Journal of Computer Assisted Radiology and Surgery 15
2020
Earlier work this paper cites.
Teed, Z. and Deng, J.: RAFT: Recurrent All-Pairs Field Transforms for Optical Flow. In: Computer Vision–ECCV 2020: 16th European Conference (2020)
2020
Earlier work this paper cites.
Colleoni, E., Edwards, P. and Stoyanov, D.: Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Teed, Z. and Deng, J.: RAFT-3D: Scene Flow using Rigid-Motion Embeddings. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Roß, T., Reinke, A., Full, P.M., Wagner, M., Kenngott, H., Apitz, M., Hempe, H., Mindroc-Filimon, D., Scholz, P., Tran, T.N. and Bruno, P.: Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge. Medical image analysis, 70, p.101920. (2021)
2021
Earlier work this paper cites.
Ding, X. and Li, X.: Exploring segment-level semantics for online phase recognition from surgical videos. IEEE Transactions on Medical Imaging 41
2022
Earlier work this paper cites.
Doersch, C., Gupta, A., Markeeva, L., Recasens, A., Smaira, L., Aytar, Y., Carreira, J., Zisserman, A. and Yang, Y.: TAP-Vid: A Benchmark for Tracking Any Point in a Video. In: Advances in Neural Information Processing Systems (2022)
2022
Cited alongside, same era.
Lüddecke, T. and Ecker, A.: Image Segmentation Using Text and Image Prompts. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Cited alongside, same era.
Cheng, H.K. and Schwing, A.G.: XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model. In: European Conference on Computer Vision (2022)
2022
Cited alongside, same era.
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q. and Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In European Conference on Computer Vision. (2022)
2022
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Mazurowski, M.A., Dong, H., Gu, H., Yang, J., Konz, N. and Zhang, Y.: Segment anything model for medical image analysis: An experimental study. Medical Image Analysis 89
2023
Later among the works it cites.
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Yip, M., Salcudean, S., Goldberg, K., Althoefer, K., Menciassi, A., Opfermann, J.D., Krieger, A., Swaminathan, K., Walsh, C.J., Huang, H. and Lee, I.C.: Artificial intelligence meets medical robotics. Science 381
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Ke, L., Ye, M., Danelljan, M., Tai, Y.W., Tang, C.K. and Yu, F.: Segment anything in high quality. In: Advances in Neural Information Processing Systems (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Zheng, Y., Harley, A.W., Shen, B., Wetzstein, G. and Guibas, L.J.: PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2023)
2023
Cited alongside, same era.
2023
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Cheng, H.K., Oh, S.W., Price, B., Schwing, A. and Lee, J.Y.: Tracking Anything with Decoupled Video Segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2023)
2023
Later among the works it cites.
Zhou, Z., Alabi, O., Wei, M., Vercauteren, T. and Shi, M.: Text Promptable Surgical Instrument Segmentation with Vision-Language Models. In: Advances in Neural Information Processing Systems (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
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2023
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Ma, J., He, Y., Li, F., Han, L., You, C. and Wang, B.: Segment anything in medical images. Nature Communications 15
2024
Closest in time.
Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C. and Liu, S.: Segment anything model for medical images? Medical Image Analysis 92
2024
Closest in time.