Fetching the paper…
Reading the bibliography…
Robot-assisted surgery has made significant progress, with instrument segmentation being a critical factor in surgical intervention quality.
H. W. Kuhn, “The hungarian method for the assignment problem,” Naval research logistics quarterly , vol. 2, no. 1-2, pp. 83–97, 1955
1955
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
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
Earlier work this paper cites.
R. Hu, M. Rohrbach, and T. Darrell, “Segmentation from natural language expressions,” in Computer Vision–ECCV 2016: 14th European Conference, Proceedings, Part I 14 . Springer, 2016, pp. 108–124
2016
Earlier work this paper cites.
A. P. Twinanda et al. , “Endonet: a deep architecture for recognition tasks on laparoscopic videos,” IEEE transactions on medical imaging , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang et al. , “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
D. Bouget, M. Allan et al. , “Vision-based and marker-less surgical tool detection and tracking: a review of the literature,” Medical image analysis , vol. 35, pp. 633–654, 2017
2017
Earlier work this paper cites.
D. Sarikaya, J. J. Corso, and K. A. Guru, “Detection and localization of robotic tools in robot-assisted surgery videos using deep neural networks for region proposal and detection,” IEEE transactions on medical imaging , vol. 36, no. 7, pp. 1542–1549, 2017
2017
Earlier work this paper cites.
T. Osa, N. Sugita, and M. Mitsuishi, “Online trajectory planning and force control for automation of surgical tasks,” IEEE Transactions on Automation Science and Engineering , vol. 15, no. 2, pp. 675–691, 2017
2017
Earlier work this paper cites.
J. Carreira and A. Zisserman, “Quo vadis, action recognition? a new model and the kinetics dataset,” in proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 6299–6308
2017
Earlier work this paper cites.
A. Vaswani et al. , “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
T.-Y. Lin et al. , “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2117–2125
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
K. Gavrilyuk, A. Ghodrati et al. , “Actor and action video segmentation from a sentence,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 5958–5966
2018
Earlier work this paper cites.
X. Wang, Y. Ye, and A. Gupta, “Zero-shot recognition via semantic embeddings and knowledge graphs,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 6857–6866
2018
Earlier work this paper cites.
D. Zang, G.-B. Bian, Y. Wang, and Z. Li, “An extremely fast and precise convolutional neural network for recognition and localization of cataract surgical tools,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China . Springer, 2019, pp. 56–64
2019
Earlier work this paper cites.
2019
Cited alongside, same era.
C. Cao and R. J. Cerfolio, “Virtual or augmented reality to enhance surgical education and surgical planning,” Thoracic surgery clinics , vol. 29, no. 3, pp. 329–337, 2019
2019
Cited alongside, same era.
S. Sheik-Ali, H. Edgcombe, and C. Paton, “Next-generation virtual and augmented reality in surgical education: a narrative review,” Surgical technology international , vol. 33, 2019
2019
Cited alongside, same era.
M. Pfeiffer, I. Funke et al. , “Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019 . Springer, 2019, pp. 119–127
2019
Cited alongside, same era.
M. Grammatikopoulou et al. , “Cadis: Cataract dataset for surgical rgb-image segmentation,” Medical Image Analysis , vol. 71, p. 102053, 2021
2021
Later among the works it cites.
X. Zhu, S. Lyu et al. , “Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 2778–2788
2021
Later among the works it cites.
Y. Long, J. Y. Wu et al. , “Relational graph learning on visual and kinematics embeddings for accurate gesture recognition in robotic surgery,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 13 346–13 353
2021
Later among the works it cites.
L. Yang, Y. Gu, G. Bian, and Y. Liu, “Drr-net: A dense-connected residual recurrent convolutional network for surgical instrument segmentation from endoscopic images,” IEEE Transactions on Medical Robotics and Bionics , 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
2020
Cited alongside, same era.
Z.-L. Ni, G.-B. Bian et al. , “Attention-guided lightweight network for real-time segmentation of robotic surgical instruments,” in 2020 IEEE international conference on robotics and automation (ICRA) . IEEE, 2020, pp. 9939–9945
2020
Cited alongside, same era.
S. Seo, J.-Y. Lee, and B. Han, “Urvos: Unified referring video object segmentation network with a large-scale benchmark,” in Computer Vision–ECCV 2020 . Springer, 2020, pp. 208–223
2020
Cited alongside, same era.
C. González et al. , “Isinet: an instance-based approach for surgical instrument segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
T. Ross, A. Reinke, Full et al. , “Comparative validation of multi-instance instrument segmentation in endoscopy: results of the robust-mis 2019 challenge,” Medical image analysis , vol. 70, p. 101920, 2021
2021
Cited alongside, same era.
X. Gao, Y. Jin et al. , “Future frame prediction for robot-assisted surgery,” in Information Processing in Medical Imaging: IPMI 2021 . Springer, 2021, pp. 533–544
2021
Cited alongside, same era.
E. Colleoni et al. , “Ssis-seg: Simulation-supervised image synthesis for surgical instrument segmentation,” IEEE Transactions on Medical Imaging , vol. 41, no. 11, pp. 3074–3086, 2022
2022
Later among the works it cites.
H. Cui, W. Dai, Y. Zhu et al. , “Braingb: a benchmark for brain network analysis with graph neural networks,” IEEE Transactions on Medical Imaging , 2022
2022
Later among the works it cites.
A. Wang, M. Islam et al. , “Rethinking surgical instrument segmentation: A background image can be all you need,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2022 . Springer, 2022
2022
Later among the works it cites.
Z. Ding, T. Hui et al. , “Language-bridged spatial-temporal interaction for referring video object segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 4964–4973
2022
Later among the works it cites.
A. Botach, E. Zheltonozhskii, and C. Baskin, “End-to-end referring video object segmentation with multimodal transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 4985–4995
2022
Later among the works it cites.
Z. Liu, J. Ning et al. , “Video swin transformer,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 3202–3211
2022
Later among the works it cites.
2023
Closest in time.
W. Shen, Y. Wang, M. Liu et al. , “Branch aggregation attention network for robotic surgical instrument segmentation,” IEEE Transactions on Medical Imaging , 2023
2023
Closest in time.
L. Sestini et al. , “Fun-sis: A fully unsupervised approach for surgical instrument segmentation,” Medical Image Analysis , p. 102751, 2023
2023
Closest in time.
T. Zhu, G. Yao, D. Hu et al. , “Data-driven morphological feature perception of single neuron with graph neural network,” IEEE Transactions on Medical Imaging , 2023
2023
Closest in time.