Fetching the paper…
Reading the bibliography…
Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective.
S. Ben-David et al. , “A theory of learning from different domains,” Machine Learning , vol. 79, no. 1, pp. 151–175, 2010
2010
Earlier work this paper cites.
I. Goodfellow et al. , “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
T. Tommasi, M. Lanzi, P. Russo, and B. Caputo, “Learning the roots of visual domain shift,” in European Conference on Computer Vision . Springer, 2016, pp. 475–482
2016
Earlier work this paper cites.
M. Zreik et al. , “Automatic segmentation of the left ventricle in cardiac ct angiography using convolutional neural networks,” in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2016, pp. 40–43
2016
Earlier work this paper cites.
F. Isensee, P. F. Jaeger, P. M. Full, I. Wolf, S. Engelhardt, and K. H. Maier-Hein, “Automatic cardiac disease assessment on cine-mri via time-series segmentation and domain specific features,” in International workshop on statistical atlases and computational models of the heart . Springer, 2017, pp. 120–129
2017
Earlier work this paper cites.
M. Ghafoorian 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
Earlier work this paper cites.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2223–2232
2017
Earlier work this paper cites.
A. Vaswani et al. , “Attention is all you need,” in NIPS , 2017, pp. 6000–6010
2017
Earlier work this paper cites.
Y. Zhang, S. Miao, T. Mansi, and R. Liao, “Task driven generative modeling for unsupervised domain adaptation: Application to x-ray image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 599–607
2018
Earlier work this paper cites.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7794–7803
2018
Earlier work this paper cites.
P. Shaw, J. Uszkoreit, and A. Vaswani, “Self-attention with relative position representations,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) , 2018, pp. 464–468
2018
Cited alongside, same era.
F. M. Carlucci, A. D’Innocente, S. Bucci, B. Caputo, and T. Tommasi, “Domain generalization by solving jigsaw puzzles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 2229–2238
2019
Cited alongside, same era.
Q. Dou et al. , “Pnp-adanet: Plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation,” IEEE Access , vol. 7, pp. 99 065–99 076, 2019
2019
Cited alongside, same era.
Y. Cao, J. Xu, S. Lin, F. Wei, and H. Hu, “Gcnet: Non-local networks meet squeeze-excitation networks and beyond,” in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , 2019
2019
A. Dosovitskiy et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
Z. Tang et al. , “Automated segmentation of retinal nonperfusion area in fluorescein angiography in retinal vein occlusion using convolutional neural networks.” Medical Physics , vol. 48, no. 2, pp. 648–658, 2021
2021
Closest in time.
M. Ren, N. Dey, J. Fishbaugh, and G. Gerig, “Segmentation-renormalized deep feature modulation for unpaired image harmonization,” IEEE Transactions on Medical Imaging , vol. 40, no. 6, pp. 1519–1530, 2021
2021
Closest in time.
Y. Yuan et al. , “Ocnet: Object context for semantic segmentation,” International Journal of Computer Vision , pp. 1–24, 2021
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
P. Ramachandran, N. Parmar, A. Vaswani, I. Bello, A. Levskaya, and J. Shlens, “Stand-alone self-attention in vision models,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
I. Bello, B. Zoph, A. Vaswani, J. Shlens, and Q. V. Le, “Attention augmented convolutional networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2019
2019
Cited alongside, same era.
X. Zhuang et al. , “Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge,” Medical image analysis , vol. 58, p. 101537, 2019
2019
Cited alongside, same era.
J. M. N. do Rio et al. , “Deep learning-based segmentation and quantification of retinal capillary non-perfusion on ultra-wide-field retinal fluorescein angiography.” Journal of Clinical Medicine , vol. 9, no. 8, p. 2537, 2020
2020
Cited alongside, same era.
S. Zhao et al. , “A review of single-source deep unsupervised visual domain adaptation.” IEEE Transactions on Neural Networks , pp. 1–21, 2020
2020
Cited alongside, same era.
F. Wu and X. Zhuang, “Cf distance: A new domain discrepancy metric and application to explicit domain adaptation for cross-modality cardiac image segmentation,” IEEE Transactions on Medical Imaging , vol. 39, no. 12, pp. 4274–4285, 2020
2020
Cited alongside, same era.
J. Jiang et al. , “Psigan: joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation-based mri segmentation,” IEEE Transactions on Medical Imaging , vol. 39, no. 12, pp. 4071–4084, 2020
2020
Cited alongside, same era.
2021
Closest in time.
2021
Closest in time.
A. Srinivas, T.-Y. Lin, N. Parmar, J. Shlens, P. Abbeel, and A. Vaswani, “Bottleneck transformers for visual recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 16 519–16 529
2021
Closest in time.
2021
Closest in time.
Z. Zheng and Y. Yang, “Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation,” International Journal of Computer Vision , vol. 129, no. 4, pp. 1106–1120, 2021
2021
Closest in time.
X. Luo et al. , “Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2021, pp. 318–329
2021
Closest in time.