Fetching the paper…
Reading the bibliography…
In this paper, we aim to improve the performance of semantic image segmentation in a semi-supervised setting in which training is effectuated with a reduced set of annotated images and additional non-annotated images.
1902
Earlier work this paper cites.
1903
Earlier work this paper cites.
doi:10.1109/T-C.1970.222832
D. Cooper, J. Freeman, On the asymptotic improvement in the outcome of supervised learning provided by additional nonsupervised learning, IEEE Transactions on Computers 19 (11) (1970) 1055–1063 · 1970
Earlier work this paper cites.
A. P. Dempster, N. M. Laird, D. B. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society, Series B 39 (1) (1977) 1–38
1977
Earlier work this paper cites.
A. Blum, T. Mitchell, Combining labeled and unlabeled data with co-training, in: Proceedings of the eleventh annual conference on Computational learning theory, ACM, 1998, pp. 92–100
1998
Earlier work this paper cites.
K. Nigam, R. Ghani, Understanding the behavior of co-training, in: Proceedings of KDD-2000 workshop on text mining, Citeseer, 2000, pp. 15–17
2000
Earlier work this paper cites.
A. Levin, P. A. Viola, Y. Freund, Unsupervised improvement of visual detectors using co-training., in: ICCV, Vol. 1, 2003, pp. 626–633
2003
Earlier work this paper cites.
B. Maeireizo, D. Litman, R. Hwa, Co-training for predicting emotions with spoken dialogue data, in: Proceedings of the ACL 2004 on Interactive poster and demonstration sessions, Association for Computational Linguistics, 2004, p. 28
2004
Earlier work this paper cites.
Y. Grandvalet, Y. Bengio, Entropy regularization, in: O. Chapelle, B. Schölkopf, A. Zien (Eds.), Semi-Supervised Learning, MIT Press, 2006, pp. 151–168
2006
Earlier work this paper cites.
X. Wan, Co-training for cross-lingual sentiment classification, in: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1-volume 1, Association for Computational Linguistics, 2009, pp. 235–243
2009
Earlier work this paper cites.
A. Vezhnevets, J. M. Buhmann, Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2010, pp. 3249–3256
2010
Earlier work this paper cites.
O. Chapelle, B. Schlkopf, A. Zien, Semi-Supervised Learning, 1st Edition, The MIT Press, 2010
2010
Earlier work this paper cites.
D. P. Kingma, S. Mohamed, D. Jimenez Rezende, M. Welling, Semi-supervised learning with deep generative models, in: Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27, Curran Associates, Inc., 2014, pp. 3581–3589
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in: Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27, Curran Associates, Inc., 2014, pp. 2672–2680
2014
Earlier work this paper cites.
H. Noh, S. Hong, B. Han, Learning deconvolution network for semantic segmentation, in: Proceedings of the IEEE international conference on computer vision, 2015, pp. 1520–1528
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, 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.
P. O. Pinheiro, R. Collobert, Weakly supervised semantic segmentation with convolutional networks, in: CVPR, Vol. 2, Citeseer, 2015, p. 6
2015
Cited alongside, same era.
D. Pathak, P. Krahenbuhl, T. Darrell, Constrained convolutional neural networks for weakly supervised segmentation, in: Proceedings of the IEEE international conference on computer vision, 2015, pp. 1796–1804
2015
Cited alongside, same era.
J. Dai, K. He, J. Sun, Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation, in: Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1635–1643
2015
Cited alongside, same era.
P. O. Pinheiro, R. Collobert, From image-level to pixel-level labeling with convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1713–1721
2015
Cited alongside, same era.
S. Gupta, J. Hoffman, J. Malik, Cross modal distillation for supervision transfer, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2827–2836
2016
Later among the works it cites.
doi:10.1016/j.media.2017.07.005
G. J. S. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, C. I. Sánchez, A survey on deep learning in medical image analysis, Medical Image Analysis 42 (2017) 60–88 · 2017
Later among the works it cites.
L. Wang, D. Nie, G. Li, É. Puybareau, J. Dolz, Q. Zhang, F. Wang, J. Xia, Z. Wu, J. Chen, et al., Benchmark on automatic 6-month-old infant brain segmentation algorithms: The iseg-2017 challenge, IEEE transactions on medical imaging
2017
Later among the works it cites.
M. Rajchl, M. C. Lee, O. Oktay, K. Kamnitsas, J. Passerat-Palmbach, W. Bai, M. Damodaram, M. A. Rutherford, J. V. Hajnal, B. Kainz, et al., Deepcut: Object segmentation from bounding box annotations using convolutional neural networks, IEEE transactions on medical imaging 36 (2) (2017) 674–683
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Rasmus, M. Berglund, M. Honkala, H. Valpola, T. Raiko, Semi-supervised learning with ladder networks, in: C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28, Curran Associates, Inc., 2015, pp. 3546–3554
2015
Cited alongside, same era.
I. Goodfellow, J. Shlens, C. Szegedy, Explaining and harnessing adversarial examples, in: International Conference on Learning Representations, 2015, p. 1
2015
Cited alongside, same era.
F. Milletari, N. Navab, S.-A. Ahmadi, V-net: Fully convolutional neural networks for volumetric medical image segmentation, in: 3D Vision (3DV), 2016 Fourth International Conference on, IEEE, 2016, pp. 565–571
2016
Cited alongside, same era.
A. Kolesnikov, C. H. Lampert, Seed, expand and constrain: Three principles for weakly-supervised image segmentation, in: European Conference on Computer Vision, Springer, 2016, pp. 695–711
2016
Cited alongside, same era.
D. Lin, J. Dai, J. Jia, K. He, J. Sun, Scribblesup: Scribble-supervised convolutional networks for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 3159–3167
2016
Cited alongside, same era.
A. Bearman, O. Russakovsky, V. Ferrari, L. Fei-Fei, What’s the point: Semantic segmentation with point supervision, in: European Conference on Computer Vision, Springer, 2016, pp. 549–565
2016
Cited alongside, same era.
Y. Wei, X. Liang, Y. Chen, Z. Jie, Y. Xiao, Y. Zhao, S. Yan, Learning to segment with image-level annotations, Pattern Recognition 59 (2016) 234–244
2016
Cited alongside, same era.
X. Qi, Z. Liu, J. Shi, H. Zhao, J. Jia, Augmented feedback in semantic segmentation under image level supervision, in: European Conference on Computer Vision, Springer, 2016, pp. 90–105
2016
Cited alongside, same era.
Q. Hou, M.-M. Cheng, X. Hu, A. Borji, Z. Tu, P. Torr, Deeply supervised salient object detection with short connections, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017, pp. 5300–5309
2017
Later among the works it cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, et al., Grad-cam: Visual explanations from deep networks via gradient-based localization., in: ICCV, 2017, pp. 618–626
2017
Later among the works it cites.
W. Bai, O. Oktay, M. Sinclair, H. Suzuki, M. Rajchl, G. Tarroni, B. Glocker, A. King, P. M. Matthews, D. Rueckert, Semi-supervised learning for network-based cardiac mr image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2017, pp. 253–260
2017
Later among the works it cites.
C. Baur, S. Albarqouni, N. Navab, Semi-supervised deep learning for fully convolutional networks, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2017, pp. 311–319
2017
Later among the works it cites.
N. Souly, C. Spampinato, M. Shah, Semi supervised semantic segmentation using generative adversarial network, in: Computer Vision (ICCV), 2017 IEEE International Conference on, IEEE, 2017, pp. 5689–5697
2017
Later among the works it cites.
Y. Zhang, L. Yang, J. Chen, M. Fredericksen, D. P. Hughes, D. Z. Chen, Deep adversarial networks for biomedical image segmentation utilizing unannotated images, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2017, pp. 408–416
2017
Later among the works it cites.
G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. van der Laak, B. Van Ginneken, C. I. Sánchez, A survey on deep learning in medical image analysis, Medical image analysis 42 (2017) 60–88
2017
Later among the works it cites.
F. Prados, J. Ashburner, C. Blaiotta, T. Brosch, J. Carballido-Gamio, M. J. Cardoso, B. N. Conrad, E. Datta, G. Dávid, B. De Leener, et al., Spinal cord grey matter segmentation challenge, Neuroimage 152 (2017) 312–329
2017
Later among the works it cites.
W.-C. Hung, Y.-H. Tsai, Y.-T. Liou, Y.-Y. Lin, M.-H. Yang, Adversarial learning for semi-supervised semantic segmentation, in: Proceedings of the British Machine Vision Conference (BMVC), 2018, p. 1
2018
Later among the works it cites.
A. Oliver, A. Odena, C. A. Raffel, E. D. Cubuk, I. Goodfellow, Realistic evaluation of deep semi-supervised learning algorithms, in: S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett (Eds.), Advances in Neural Information Processing Systems 31, Curran Associates, Inc., 2018, pp. 3239–3250
2018
Later among the works it cites.
J. Dolz, C. Desrosiers, I. Ben Ayed, 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study, NeuroImage 170 (2018) 456–470
2018
Later among the works it cites.
doi:10.1109/TMI.2018.2837502
O. B. et al., Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?, IEEE Transactions on Medical Imaging 37 (11) (2018) 2514–2525 · 2018
Later among the works it cites.