Ahsan, U., Sun, C., Essa, I., 2018. DiscrimNet: semi-supervised action recognition from videos using generative adversarial networks. arXiv preprint arXiv:1801.07230.
Berthelot, 2020, ReMixMatch: semi-supervised learning with distribution alignment and augmentation anchoring
Berthelot, 2019, MixMatch: a holistic approach to semi-supervised learning, 5050
Bodenstedt, 2019, Active learning using deep Bayesian networks for surgical workflow analysis, Int. J. Comput. Assisted Radiol. Surg., 14, 1079, 10.1007/s11548-019-01963-9
Bouget, 2017, Vision-based and marker-less surgical tool detection and tracking: a review of the literature, Med. Image Anal., 35, 633, 10.1016/j.media.2016.09.003
Bricon-Souf, 2007, Context awareness in health care: a review, Int. J. Med. Inf., 76, 2, 10.1016/j.ijmedinf.2006.01.003
Cheplygina, 2019, Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis, Med. Image Anal., 54, 280, 10.1016/j.media.2019.03.009
Cleary, 2005, OR 2020: the operating room of the future., J. Laparosc. Adv. Surg.Tech. Part A, 15, 495, 10.1089/lap.2005.15.495
da Costa Rocha, 2019, Self-supervised surgical tool segmentation using kinematic information, 8720
Dergachyova, 2016, Automatic data-driven real-time segmentation and recognition of surgical workflow, Int. J. Comput. Assist.Radiol. Surg., 11, 1081, 10.1007/s11548-016-1371-x
DiPietro, 2019, Segmenting and classifying activities in robot-assisted surgery with recurrent neural networks, Int. J. Comput. Assist.Radiol. Surg., 14, 2005, 10.1007/s11548-019-01953-x
DiPietro, 2018, Unsupervised learning for surgical motion by learning to predict the future, 281
DiPietro, 2019, Automated surgical activity recognition with one labeled sequence, 458
Funke, 2018, Temporal coherence-based self-supervised learning for laparoscopic workflow analysis, 85
Ganaye, 2019, Removing segmentation inconsistencies with semi-supervised non-adjacency constraint, Med. Image Anal., 58, 101551, 10.1016/j.media.2019.101551
Ghadiyaram, 2019, Large-scale weakly-supervised pre-training for video action recognition, 12046
Girdhar, 2019, Distinit: learning video representations without a single labeled video, 852
Grandvalet, 2005, Semi-supervised learning by entropy minimization, 529
Han, 2020, Self-supervised co-training for video representation learning, Adv. Neural Inf. Process. Syst., 33
He, 2016, Deep residual learning for image recognition, 770
Jin, 2019, Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video, 440
Jin, 2017, SV-RCNet: workflow recognition from surgical videos using recurrent convolutional network, IEEE Trans. Med. Imaging, 37, 1114, 10.1109/TMI.2017.2787657
Jin, 2019, Multi-task recurrent convolutional network with correlation loss for surgical video analysis, Med. Image Anal., 101572
Kong, 2020, Cycle-contrast for self-supervised video representation learning, Adv. Neural Inf. Process. Syst., 33
Laine, 2017, Temporal ensembling for semi-supervised learning
Lee, 2013, Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks, Vol. 3, 2
Maier-Hein, L., Eisenmann, M., Sarikaya, D., März, K., Collins, T., Malpani, A., Fallert, J., Feussner, H., Giannarou, S., Mascagni, P., et al., 2020. Surgical data science–from concepts to clinical translation. arXiv preprint arXiv:2011.02284.
Padoy, 2019, Machine and deep learning for workflow recognition during surgery, Minim. Invasive Ther. Allied Technol., 28, 82, 10.1080/13645706.2019.1584116
Padoy, 2012, Statistical modeling and recognition of surgical workflow, Med. Image Anal., 16, 632, 10.1016/j.media.2010.10.001
Qin, 2020, Temporal segmentation of surgical sub-tasks through deep learning with multiple data sources
Sajjadi, 2016, Regularization with stochastic transformations and perturbations for deep semi-supervised learning, 1163
Shi, 2020, LRTD: long-range temporal dependency based active learning for surgical workflow recognition
Shi, 2020, Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis, Med. Image Anal., 60, 101624, 10.1016/j.media.2019.101624
Tarvainen, 2017, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, 1195
Twinanda, 2016, EndoNet: a deep architecture for recognition tasks on laparoscopic videos, IEEE Trans. Med. Imaging, 36, 86, 10.1109/TMI.2016.2593957
van Amsterdam, 2020, Multi-task recurrent neural network for surgical gesture recognition and progress prediction
Wang, 2020, Self-supervised video representation learning by pace prediction
Wang, 2018, Non-local neural networks, 7794
Wang, 2019, Learning correspondence from the cycle-consistency of time, 2566
Wang, 2020, Semi-supervised mp-MRI data synthesis with StitchLayer and auxiliary distance maximization, Med. Image Anal., 59, 101565, 10.1016/j.media.2019.101565
Xia, 2020, Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation, Med. Image Anal., 65, 101766, 10.1016/j.media.2020.101766
Xie, 2020, Self-training with noisy student improves imagenet classification, 10687
Xie, 2019, Semi-supervised adversarial model for benign–malignant lung nodule classification on chest CT, Med. Image Anal., 57, 237, 10.1016/j.media.2019.07.004
Yengera, G., Mutter, D., Marescaux, J., Padoy, N., 2018. Less is more: Surgical phase recognition with less annotations through self-supervised pre-training of CNN-LSTM networks. arXiv preprint arXiv:1805.08569.
Yi, 2019, Hard frame detection and online mapping for surgical phase recognition, 449
Yu, 2019, Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition
Zheng, 2019, Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow, Med. Image Anal., 56, 80, 10.1016/j.media.2019.06.001
Zhu, 2020, Rubik’s cube+: a self-supervised feature learning framework for 3D medical image analysis, Med. Image Anal., 101746, 10.1016/j.media.2020.101746
Zisimopoulos, 2018, DeepPhase: surgical phase recognition in cataracts videos, 265