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In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis.
K. Held, E. Kops, B. Krause, W. Wells, R. Kikinis, and H.-W. Muller-Gartner, “Markov random field segmentation of brain mr images,” IEEE Transactions on Medical Imaging , vol. 16, no. 6, pp. 878–886, 1997
1997
Earlier work this paper cites.
A. Tsai, A. Yezzi, W. Wells, C. Tempany, D. Tucker, A. Fan, W. Grimson, and A. Willsky, “A shape-based approach to the segmentation of medical imagery using level sets,” IEEE Transactions on Medical Imaging , vol. 22, no. 2, pp. 137–154, 2003
2003
Earlier work this paper cites.
O. Ronneberger, P.Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI) , ser. LNCS, vol. 9351. Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
Ö. Çiçek, A. Abdulkadir, S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: Learning dense volumetric segmentation from sparse annotation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI) , ser. LNCS, vol. 9901. Springer, Oct 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,” 2016 Fourth International Conference on 3D Vision (3DV) , pp. 565–571, 2016
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 2016 Fourth International Conference on 3D Vision (3DV) , 2016, pp. 565–571
2016
Earlier work this paper cites.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 6230–6239
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , vol. 30. Curran Associates, Inc., 2017
2017
Earlier work this paper cites.
X. Xiao, S. Lian, Z. Luo, and S. Li, “Weighted res-unet for high-quality retina vessel segmentation,” 2018 9th International Conference on Information Technology in Medicine and Education (ITME) , pp. 327–331, 2018
2018
Earlier work this paper cites.
Z. Zhou, M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmentation.” Springer Verlag, 2018, pp. 3–11
2018
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 40, no. 4, pp. 834–848, 2018
2018
Earlier work this paper cites.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 7794–7803
2018
Earlier work this paper cites.
X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P.-A. Heng, “H-denseunet: Hybrid densely connected unet for liver and tumor segmentation from ct volumes,” IEEE Transactions on Medical Imaging , vol. 37, no. 12, pp. 2663–2674, 2018
2018
Earlier work this paper cites.
H. Hu, J. Gu, Z. Zhang, J. Dai, and Y. Wei, “Relation networks for object detection,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 3588–3597
2018
Cited alongside, same era.
O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz, B. Glocker, and D. Rueckert, “Attention u-net: Learning where to look for the pancreas,” IMIDL Conference , 2018
2018
Cited alongside, same era.
Z. Gu, J. Cheng, H. Fu, K. Zhou, H. Hao, Y. Zhao, T. Zhang, S. Gao, and J. Liu, “Ce-net: Context encoder network for 2d medical image segmentation,” IEEE Transactions on Medical Imaging , vol. 38, no. 10, pp. 2281–2292, 2019
2019
Cited alongside, same era.
J. Schlemper, O. Oktay, M. Schaap, M. Heinrich, B. Kainz, B. Glocker, and D. Rueckert, “Attention gated networks: Learning to leverage salient regions in medical images,” Medical Image Analysis , vol. 53, pp. 197–207, 2019
2019
Cited alongside, same era.
A. Hatamizadeh, D. Yang, H. Roth, and D. Xu, “Unetr: Transformers for 3d medical image segmentation,” 2021
2021
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2021
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K. S. P. J. M.-H. K. Isensee F, Jaeger PF, “nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,” Nat Methods , vol. 18(2):203-211, 2021
2021
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2021
2021
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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186. [Online]. Available: https://www.aclweb.org/anthology/N19-1423
2019
Cited alongside, same era.
H. Hu, Z. Zhang, Z. Xie, and S. Lin, “Local relation networks for image recognition,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 3463–3472
2019
Cited alongside, same era.
Q. Jin, Z. Meng, C. Sun, H. Cui, and R. Su, “Ra-unet: A hybrid deep attention-aware network to extract liver and tumor in ct scans,” Frontiers in Bioengineering and Biotechnology , vol. 8, p. 1471, 2020
2020
Cited alongside, same era.
H. Huang, L. Lin, R. Tong, H. Hu, Q. Zhang, Y. Iwamoto, X. Han, Y.-W. Chen, and J. Wu, “Unet 3+: A full-scale connected unet for medical image segmentation,” 2020
2020
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
S. Fu, Y. Lu, Y. Wang, Y. Zhou, W. Shen, E. Fishman, and A. Yuille, “Domain adaptive relational reasoning for 3d multi-organ segmentation,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 , 2020, pp. 656–666
2020
Cited alongside, same era.
S. Fu, Y. Lu, Y. Wang, Y. Zhou, W. Shen, E. Fishman, and A. Yuille, “Domain adaptive relational reasoning for 3d multi-organ segmentation,” Germany, 2020, pp. 656–666
2020
Cited alongside, same era.
2021
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