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Convolutional Neural Networks (CNNs) have shown to be powerful medical image segmentation models.
D. Karimi, J. M. Peters, A. Ouaalam, S. P. Prabhu, M. Sahin, D. A. Krueger, A. Kolevzon, C. Eng, S. K. Warfield, and A. Gholipour, “Learning to detect brain lesions from noisy annotations,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) , 2020, pp. 1910–1914
1914
Earlier work this paper cites.
R. Caruana, “A dozen tricks with multitask learning,” in Neural networks: tricks of the trade . Springer, 1998, pp. 165–191
1998
Earlier work this paper cites.
B. Zadrozny and C. Elkan, “Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers,” in Icml , vol. 1. Citeseer, 2001, pp. 609–616
2001
Earlier work this paper cites.
2004
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering , vol. 22, no. 10, pp. 1345–1359, 2009
2009
Earlier work this paper cites.
O. Chapelle, B. Scholkopf, and A. Zien, “Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews],” IEEE Transactions on Neural Networks , vol. 20, no. 3, pp. 542–542, 2009
2009
Earlier work this paper cites.
T. Heimann, B. Van Ginneken, M. A. Styner, Y. Arzhaeva, V. Aurich, C. Bauer, A. Beck, C. Becker, R. Beichel, G. Bekes et al. , “Comparison and evaluation of methods for liver segmentation from ct datasets,” IEEE transactions on medical imaging , vol. 28, no. 8, pp. 1251–1265, 2009
2009
Earlier work this paper cites.
2013
Earlier work this paper cites.
G. Golub and C. F. Van-Loan, Matrix Computations . Baltimore, MD: Johns Hopkins University Press, 2013
2013
Earlier work this paper cites.
Y. Bengio, G. Mesnil, Y. Dauphin, and S. Rifai, “Better mixing via deep representations,” in International conference on machine learning , 2013, pp. 552–560
2013
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proceedings of the 3rd International Conference on Learning Representations (ICLR) , 2014
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, p. 436, 2015
2015
Earlier work this paper cites.
N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and J. Liang, “Convolutional neural networks for medical image analysis: full training or fine tuning?” IEEE transactions on medical imaging , vol. 35, no. 5, pp. 1299–1312, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
P. Moeskops, J. M. Wolterink, B. H. van der Velden, K. G. Gilhuijs, T. Leiner, M. A. Viergever, and I. Išgum, “Deep learning for multi-task medical image segmentation in multiple modalities,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2016, pp. 478–486
2016
Earlier work this paper cites.
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2016, pp. 424–432
2016
Earlier work this paper cites.
F. Milletari, N. Navab, and 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
Earlier work this paper cites.
2017
Cited alongside, same era.
M. Ghafoorian, A. Mehrtash, T. Kapur, N. Karssemeijer, E. Marchiori, M. Pesteie, C. R. Guttmann, F.-E. de Leeuw, C. M. Tempany, B. van Ginneken et al. , “Transfer learning for domain adaptation in mri: Application in brain lesion segmentation,” in International conference on medical image computing and computer-assisted intervention . Springer, 2017, pp. 516–524
2017
Cited alongside, same era.
C. Baur, S. Albarqouni, and 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
Cited alongside, same era.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” in Advances in Neural Information Processing Systems , 2017, pp. 6402–6413
2018
Later among the works it cites.
2018
Later among the works it cites.
D. Karimi, G. Samei, C. Kesch, G. Nir, and S. E. Salcudean, “Prostate segmentation in mri using a convolutional neural network architecture and training strategy based on statistical shape models,” International Journal of Computer Assisted Radiology and Surgery , vol. 13, no. 8, pp. 1211–1219, Aug 2018. [Online]. Available: https://doi.org/10.1007/s11548-018-1785-8
2018
Later among the works it cites.
V. Cheplygina, M. de Bruijne, and J. P. Pluim, “Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis,” Medical image analysis , vol. 54, pp. 280–296, 2019
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2017
Cited alongside, same era.
A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” in Advances in neural information processing systems , 2017, pp. 5574–5584
2017
Cited alongside, same era.
2017
Cited alongside, same era.
F. Isensee, P. Kickingereder, W. Wick, M. Bendszus, and K. H. Maier-Hein, “No new-net,” in International MICCAI Brainlesion Workshop . Springer, 2018, pp. 234–244
2018
Cited alongside, same era.
K. Lee, K. Lee, H. Lee, and J. Shin, “A simple unified framework for detecting out-of-distribution samples and adversarial attacks,” in Advances in Neural Information Processing Systems , 2018, pp. 7167–7177
2018
Cited alongside, same era.
A. Harouni, A. Karargyris, M. Negahdar, D. Beymer, and T. Syeda-Mahmood, “Universal multi-modal deep network for classification and segmentation of medical images,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) . IEEE, 2018, pp. 872–876
2018
Cited alongside, same era.
M. Ghafoorian, J. Teuwen, R. Manniesing, F.-E. de Leeuw, B. van Ginneken, N. Karssemeijer, and B. Platel, “Student beats the teacher: deep neural networks for lateral ventricles segmentation in brain mr,” in Medical Imaging 2018: Image Processing , vol. 10574. International Society for Optics and Photonics, 2018, p. 105742U
2018
Cited alongside, same era.
L. Zhang, V. Gopalakrishnan, L. Lu, R. M. Summers, J. Moss, and J. Yao, “Self-learning to detect and segment cysts in lung ct images without manual annotation,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) . IEEE, 2018, pp. 1100–1103
2018
Cited alongside, same era.
A. Oliver, A. Odena, C. A. Raffel, E. D. Cubuk, and I. Goodfellow, “Realistic evaluation of deep semi-supervised learning algorithms,” in Advances in Neural Information Processing Systems , 2018, pp. 3235–3246
2018
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
J. Enguehard, P. O’Halloran, and A. Gholipour, “Semi-supervised learning with deep embedded clustering for image classification and segmentation,” IEEE Access , vol. 7, pp. 11 093–11 104, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
J. A. Fries, P. Varma, V. S. Chen, K. Xiao, H. Tejeda, P. Saha, J. Dunnmon, H. Chubb, S. Maskatia, M. Fiterau et al. , “Weakly supervised classification of aortic valve malformations using unlabeled cardiac mri sequences,” BioRxiv , p. 339630, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
G. Wang, W. Li, M. Aertsen, J. Deprest, S. Ourselin, and T. Vercauteren, “Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks,” Neurocomputing , vol. 338, pp. 34 – 45, 2019. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0925231219301961
2019
Later among the works it cites.
D. Karimi, Q. Zeng, P. Mathur, A. Avinash, S. Mahdavi, I. Spadinger, P. Abolmaesumi, and S. E. Salcudean, “Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images,” Medical Image Analysis , vol. 57, pp. 186 – 196, 2019. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1361841519300623
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
M. Bastiani, J. L. Andersson, L. Cordero-Grande, M. Murgasova, J. Hutter, A. N. Price, A. Makropoulos, S. P. Fitzgibbon, E. Hughes, D. Rueckert et al. , “Automated processing pipeline for neonatal diffusion mri in the developing human connectome project,” NeuroImage , vol. 185, pp. 750–763, 2019
2019
Later among the works it cites.
A. Kavur, M. Selver, O. Dicle, M. Barıs, and N. Gezer, “Chaos-combined (ct-mr) healthy abdominal organ segmentation challenge data. accessed: 2019-04-11,” 2019
2019
Later among the works it cites.