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In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison.
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D. Bouget, R. Benenson, M. Omran, L. Riffaud, B. Schiele, and P. Jannin, “Detecting surgical tools by modelling local appearance and global shape,” TMI , 2015
2015
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
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2015
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2015
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T. Kurmann, P. Marquez Neila, X. Du, P. Fua, D. Stoyanov, and S. Wolf, “Simultaneous recognition and pose estimation of instruments in minimally invasive surgery,” in MICCAI . Springer, 2017
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L. C. García-Peraza-Herrera, W. Li, L. Fidon, C. Gruijthuijsen, A. Devreker, G. Attilakos, J. Deprest, E. V. Poorten, D. Stoyanov, T. Vercauteren, and S. Ourselin, “Toolnet: Holistically-nested real-time segmentation of robotic surgical tools,” in arXiv , 2017
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2015
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 3431–3440
2015
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I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab, “Deeper depth prediction with fully convolutional residual networks,” in 3D Vision (3DV), 2016 Fourth International Conference on . IEEE, 2016, pp. 239–248
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
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M. Allan, S. Ourselin, D. J. Hawkes, J. D. Kelly, and D. Stoyanov, “3-d pose estimation of articulated instruments in robotic minimally invasive surgery,” in TMI , 2017
2017
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I. Laina, N. Rieke, C. Rupprecht, J. P. Vizcaíno, A. Eslami, F. Tombari, and N. Navab, “Concurrent segmentation and localization for tracking of surgical instruments,” in MICCAI . Springer, 2017
2017
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M. D. Kohli, R. M. Summers, and J. R. Geis, “Medical image data and datasets in the era of machine learning—whitepaper from the 2016 c-mimi meeting dataset session,” Journal of digital imaging , vol. 30, no. 4, pp. 392–399, 2017
2017
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F. Milletari, N. Rieke, M. Baust, M. Esposito, and N. Navab, “Cfcm: Segmentation via coarse to fine context memory,” in Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV , 2018, pp. 667–674
2018
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2018
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Y.-H. Su, K. Huang, and B. Hannaford, “Real-time vision-based surgical tool segmentation with robot kinematics prior,” in Medical Robotics (ISMR), 2018 International Symposium on . IEEE, 2018, pp. 1–6
2018
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