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Real-time algorithms for automatically recognizing surgical phases are needed to develop systems that can provide assistance to surgeons, enable better management of operating room (OR) resources and consequently improve safety within the OR.
Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., Frasconi, P., 1994 · 1994
Earlier work this paper cites.
Long short-term memory
Hochreiter, S., Schmidhuber, J., 1997 · 1997
Earlier work this paper cites.
Greedy layer-wise training of deep networks, in: Proceedings of the 19th International Conference on Neural Information Processing Systems, MIT Press, Cambridge, MA, USA. pp. 153–160
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., 2006 · 2006
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
Hinton, G.E., Salakhutdinov, R.R., 2006 · 2006
Earlier work this paper cites.
On-line recognition of surgical activity for monitoring in the operating room, in: Proceedings of the 20th National Conference on Innovative Applications of Artificial Intelligence - Volume 3, AAAI Press. pp. 1718–1724
Padoy, N., Blum, T., Feussner, H., Berger, M.O., Navab, N., 2008 · 2008
Earlier work this paper cites.
Deep learning from temporal coherence in video, in: Proceedings of the 26th Annual International Conference on Machine Learning, ACM, New York, NY, USA. pp. 737–744
Mobahi, H., Collobert, R., Weston, J., 2009 · 2009
Earlier work this paper cites.
Modeling and segmentation of surgical workflow from laparoscopic video, in: Proceedings of the 13th International Conference on Medical Image Computing and Computer-assisted Intervention: Part III, Springer-Verlag, Berlin, Heidelberg. pp. 400–407
Blum, T., Feussner, H., Navab, N., 2010 · 2010
Earlier work this paper cites.
Statistical modeling and recognition of surgical workflow
Padoy, N., Blum, T., Ahmadi, S.A., Feussner, H., Berger, M.O., Navab, N., 2012 · 2010
Earlier work this paper cites.
A framework for the recognition of high-level surgical tasks from video images for cataract surgeries
Lalys, F., Riffaud, L., Bouget, D., Jannin, P., 2012 · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks, in: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, Curran Associates Inc., USA. pp. 1097–1105
Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012 · 2012
Earlier work this paper cites.
Multi-site study of surgical practice in neurosurgery based on surgical process models
Forestier, G., Lalys, F., Riffaud, L., Collins, D.L., Meixensberger, J., Wassef, S.N., Neumuth, T., Goulet, B., Jannin, P., 2013 · 2013
Earlier work this paper cites.
Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures
Lalys, F., Bouget, D., Riffaud, L., Jannin, P., 2013 · 2013
Earlier work this paper cites.
Learning phrase representations using rnn encoder–decoder for statistical machine translation, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Doha, Qatar. pp. 1724–1734
Cho, K., van Merriënboer, B., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y., 2014 · 2014
Earlier work this paper cites.
Discriminative unsupervised feature learning with convolutional neural networks, in: NIPS
Dosovitskiy, A., Springenberg, J.T., Riedmiller, M., Brox, T., 2014 · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation, in: Computer Vision and Pattern Recognition
Girshick, R., Donahue, J., Darrell, T., Malik, J., 2014 · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding, in: Proceedings of the 22Nd ACM International Conference on Multimedia, ACM, New York, NY, USA. pp. 675–678
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T., 2014 · 2014
Earlier work this paper cites.
Knowledge-driven formalization of laparoscopic surgeries for rule-based intraoperative context-aware assistance, in: Stoyanov, D., Collins, D.L., Sakuma, I., Abolmaesumi, P., Jannin, P. (Eds.), Information Processing in Computer-Assisted Interventions, Springer International Publishing, Cham. pp. 158–167
Katić, D., Wekerle, A.L., Gärtner, F., Kenngott, H., Müller-Stich, B.P., Dillmann, R., Speidel, S., 2014 · 2014
Cited alongside, same era.
An analysis of unsupervised pre-training in light of recent advances
Paine, T.L., Khorrami, P., Han, W., Huang, T.S., 2014 · 2014
Cited alongside, same era.
Real-time segmentation and recognition of surgical tasks in cataract surgery videos
Quellec, G., Lamard, M., Cochener, B., Cazuguel, G., 2014 · 2014
Cited alongside, same era.
Real-time task recognition in cataract surgery videos using adaptive spatiotemporal polynomials
Quellec, G., Lamard, M., Cochener, B., Cazuguel, G., 2015 · 2014
Cited alongside, same era.
EndoRCN: recurrent convolutional networks for recognition of surgical workflow in cholecystectomy procedure video
Jin, Y., Dou, Q., Chen, H., Yu, L., Heng, P.A., 2016 · 2016
Later among the works it cites.
Learning representations for automatic colorization, in: European Conference on Computer Vision (ECCV)
Larsson, G., Maire, M., Shakhnarovich, G., 2016 · 2016
Later among the works it cites.
Shuffle and learn: Unsupervised learning using temporal order verification, in: ECCV
Misra, I., Zitnick, C.L., Hebert, M., 2016 · 2016
Later among the works it cites.
Unsupervised learning of visual representions by solving jigsaw puzzles, in: ECCV
Noroozi, M., Favaro, P., 2016 · 2016
Later among the works it cites.
Colorful image colorization, in: ECCV
Zhang, R., Isola, P., Efros, A.A., 2016 · 2016
Later among the works it cites.
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Learning to see by moving, in: The IEEE International Conference on Computer Vision (ICCV)
Agrawal, P., Carreira, J., Malik, J., 2015 · 2015
Cited alongside, same era.
Unsupervised visual representation learning by context prediction, in: International Conference on Computer Vision (ICCV)
Doersch, C., Gupta, A., Efros, A.A., 2015 · 2015
Cited alongside, same era.
Automatic phase prediction from low-level surgical activities
Forestier, G., Riffaud, L., Jannin, P., 2015 · 2015
Cited alongside, same era.
Fast r-cnn, in: International Conference on Computer Vision (ICCV)
Girshick, R., 2015 · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization, in: ICLR
Kingma, D.P., Ba, J., 2015 · 2015
Cited alongside, same era.
An improved model for segmentation and recognition of fine-grained activities with application to surgical training tasks, in: 2015 IEEE Winter Conference on Applications of Computer Vision, pp. 1123–1129
Lea, C., Hager, G.D., Vidal, R., 2015 · 2015
Cited alongside, same era.
Convolutional LSTM network: A machine learning approach for precipitation nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D., Wong, W., Woo, W., 2015 · 2015
Cited alongside, same era.
Unsupervised learning of video representations using lstms, in: Bach, F., Blei, D. (Eds.), Proceedings of the 32nd International Conference on Machine Learning, PMLR, Lille, France. pp. 843–852
Srivastava, N., Mansimov, E., Salakhudinov, R., 2015 · 2015
Cited alongside, same era.
Deep neural networks predict remaining surgery duration from cholecystectomy videos, in: MICCAI, pp. 586–593
Aksamentov, I., Twinanda, A.P., Mutter, D., Marescaux, J., De Mathelin, M., Padoy, N., 2017 · 2017
Later among the works it cites.
Bodenstedt, S., Wagner, M., Katic, D., Mietkowski, P., Mayer, B.F.B., Kenngott, H., Müller-Stich, B.P., Dillmann, R., Speidel, S., 2017 · 2017
Later among the works it cites.
Real-time analysis of cataract surgery videos using statistical models
Charrière, K., Quellec, G., Lamard, M., Martiano, D., Cazuguel, G., Coatrieux, G., Cochener, B., 2017 · 2017
Later among the works it cites.
Multi-task self-supervised visual learning, in: International Conference on Computer Vision
Doersch, C., Zisserman, A., 2017 · 2017
Later among the works it cites.
Self-supervised video representation learning with odd-one-out networks, in: IEEE International Conference on Computer Vision
Fernando, B., Bilen, H., Gavves, E., Gould, S., 2017 · 2017
Later among the works it cites.
Hajj, H.A., Lamard, M., Conze, P., Cochener, B., Quellec, G., 2017 · 2017
Later among the works it cites.
Unsupervised representation learning by sorting sequence, in: IEEE International Conference on Computer Vision
Lee, H.Y., Huang, J.B., Singh, M.K., Yang, M.H., 2017 · 2017
Later among the works it cites.
Deep predictive coding networks for video prediction and unsupervised learning, in: ICLR
Lotter, W., Kreiman, G., Cox, D., 2017 · 2017
Later among the works it cites.
Vision-based Approaches for Surgical Activity Recognition Using Laparoscopic and RGBD Videos
Twinanda, A.P., 2017 · 2017
Later among the works it cites.
Endonet: A deep architecture for recognition tasks on laparoscopic videos
Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., de Mathelin, M., Padoy, N., 2017 · 2017
Later among the works it cites.
Twinanda, A.P., Yengera, G., Mutter, D., Marescaux, J., Padoy, N., 2018 · 2018
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