Surgical-Tools Tracking Based on Convolutional Neural Network and Long Short-Term Memory
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-66169-4_3
Reference9 articles.
1. Choi, F., Bareum, S.: Surgical-tools detection based on convolutional neural network in laparoscopic robot-assisted surgery. In: International Conference of the IEEE Engineering in Medicine & Biology Society (2017)
2. Gordon, F., Farhadi, A., Fox, D.: Re3: real-time recurrent regression networks for visual tracking of generic objects. IEEE Robot. Autom. Lett. 3(2), 788–795 (2018)
3. Hochreiter, F., Schmidhuber, S.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
4. Garcia-Peraza-Herrera, L., Li, W., Gruijthuijsen, C., Devreker, A., Attilakos, G., Deprest, J., Vander Poorten, E., Stoyanov, D., Vercauteren, T., Ourselin, S.: Real-time segmentation of nonrigid surgical tools based on deep learning and tracking. In: CARE Workshop (MICCAI 2016), pp. 84–95(2016)
5. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection (2016)
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