Fetching the paper…
Reading the bibliography…
Online surgical phase recognition plays a significant role towards building contextual tools that could quantify performance and oversee the execution of surgical workflows.
H. Hotelling, “Analysis of a complex of statistical variables into principal components.”
1933
Earlier work this paper cites.
H. Sakoe and S. Chiba, “Dynamic programming algorithm optimization for spoken word recognition,”
1978
Earlier work this paper cites.
L. R. Rabiner, “A tutorial on hidden markov models and selected applications in speech recognition,”
1989
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
1997
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,”
1998
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
Earlier work this paper cites.
T. Blum, H. Feußner, and N. Navab, “Modeling and segmentation of surgical workflow from laparoscopic video,” in
2010
Earlier work this paper cites.
J. E. Bardram, A. Doryab, R. M. Jensen, P. M. Lange, K. L. G. Nielsen, and S. T. Petersen, “Phase recognition during surgical procedures using embedded and body-worn sensors,” in
2011
Earlier work this paper cites.
N. Padoy, T. Blum, S. Ahmadi, H. Feußner, M. Berger, and N. Navab, “Statistical modeling and recognition of surgical workflow,”
2012
Earlier work this paper cites.
M. S. H. et al., “Feasibility of real-time workflow segmentation for tracked needle interventions,”
2014
Earlier work this paper cites.
G. Quellec, M. Lamard, B. Cochener, and G. Cazuguel, “Real-time task recognition in cataract surgery videos using adaptive spatiotemporal polynomials,”
2015
Earlier work this paper cites.
O. Dergachyova, D. Bouget, A. Huaulmé, X. Morandi, and P. Jannin, “Automatic data-driven real-time segmentation and recognition of surgical workflow,”
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Earlier work this paper cites.
C. Lea, R. Vidal, A. Reiter, and G. D. Hager, “Temporal convolutional networks: A unified approach to action segmentation,” in
2016
Earlier work this paper cites.
A. van den Oord et al., “Wavenet: A generative model for raw audio,” in
2016
Cited alongside, same era.
2016
Cited alongside, same era.
A. P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy, “Endonet: A deep architecture for recognition tasks on laparoscopic videos,”
2017
Cited alongside, same era.
A. P. Twinanda, “Vision-based approaches for surgical activity recognition using laparoscopic and RBGD videos. (approches basées vision pour la reconnaissance d’activités chirurgicales à partir de vidéos laparoscopiques et multi-vues RGBD),” Ph.D. dissertation, University of Strasbourg, France, 2017
2017
Cited alongside, same era.
A. V. et al., “Attention is all you need,” in
X. Gao, Y. Jin, Q. Dou, and P. Heng, “Automatic gesture recognition in robot-assisted surgery with reinforcement learning and tree search,” in
2020
Later among the works it cites.
T. C. et al., “Tecno: Surgical phase recognition with multi-stage temporal convolutional networks,” in
2020
Later among the works it cites.
C. R. Garrow, K.-F. Kowalewski, L. Li, M. Wagner, M. W. Schmidt, S. Engelhardt, D. A. Hashimoto, H. G. Kenngott, S. Bodenstedt, S. Speidel
2021
Later among the works it cites.
X. Gao, Y. Jin, Y. Long, Q. Dou, and P. Heng, “Trans-svnet: Accurate phase recognition from surgical videos via hybrid embedding aggregation transformer,” in
2021
Later among the works it cites.
Y. Jin, Y. Long, C. Chen, Z. Zhao, Q. Dou, and P. Heng, “Temporal memory relation network for workflow recognition from surgical video,”
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
P. G. et al., “Accurate, large minibatch SGD: training imagenet in 1 hour,”
2017
Cited alongside, same era.
Y. J. et al., “Sv-rcnet: Workflow recognition from surgical videos using recurrent convolutional network,”
2018
Cited alongside, same era.
F. Yi and T. Jiang, “Hard frame detection and online mapping for surgical phase recognition,” in
2019
Cited alongside, same era.
Y. A. Farha and J. Gall, “MS-TCN: multi-stage temporal convolutional network for action segmentation,” in
2019
Cited alongside, same era.
A. Paszke, S. Gross, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in
2019
Cited alongside, same era.
2020
Cited alongside, same era.
T. Vercauteren, M. Unberath, N. Padoy, and N. Navab, “CAI4CAI: the rise of contextual artificial intelligence in computer-assisted interventions,”
2020
Cited alongside, same era.
A. D. et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” in
2021
Later among the works it cites.
G. Bertasius, H. Wang, and L. Torresani, “Is space-time attention all you need for video understanding?” in
2021
Later among the works it cites.
A. Arnab, M. Dehghani, G. Heigold, C. Sun, M. Lucic, and C. Schmid, “Vivit: A video vision transformer,” in
2021
Later among the works it cites.
R. Girdhar and K. Grauman, “Anticipative video transformer,” in
2021
Later among the works it cites.
T. Czempiel, M. Paschali, D. Ostler, S. T. Kim, B. Busam, and N. Navab, “Opera: Attention-regularized transformers for surgical phase recognition,” in
2021
Later among the works it cites.
H. Z. et al., “Informer: Beyond efficient transformer for long sequence time-series forecasting,” in
2021
Later among the works it cites.
D. Damen, H. Doughty, G. M. Farinella, , A. Furnari, J. Ma, E. Kazakos, D. Moltisanti, J. Munro, T. Perrett, W. Price, and M. Wray, “Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100,”
2022
Later among the works it cites.
Z. Wang, B. Lu, Y. Long, F. Zhong, T.-H. Cheung, Q. Dou, and Y. Liu, “Autolaparo: A new dataset of integrated multi-tasks for image-guided surgical automation in laparoscopic hysterectomy,” in
2022
Later among the works it cites.