Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation

Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer vision (CVPR). pp. 1501–1510 (2017)

2017

Earlier work this paper cites.

Li, H., Pan, S.J., Wang, S., Kot, A.C.: Domain generalization with adversarial feature learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)

2018

Earlier work this paper cites.

Morerio, P., Cavazza, J., Murino, V.: Minimal-entropy correlation alignment for unsupervised deep domain adaptation. In: International Conference on Learning Representations (2018), https://openreview.net/forum?id=rJWechg0Z

2018

Earlier work this paper cites.

Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. In: International Conference on Learning Representations (2018), https://openreview.net/forum?id=r1Ddp1-Rb

2018

Earlier work this paper cites.

Chen, C., Qin, C., Qiu, H., Ouyang, C., Wang, S., Chen, L., Tarroni, G., Bai, W., Rueckert, D.: Realistic adversarial data augmentation for mr image segmentation. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23. pp. 667–677. Springer (2020)

2020

Earlier work this paper cites.

Huang, Z., Wang, H., Xing, E.P., Huang, D.: Self-challenging improves cross-domain generalization. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16. pp. 124–140. Springer (2020)

2020

Earlier work this paper cites.

Liu, Q., Dou, Q., Heng, P.A.: Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II 23. pp. 475–485. Springer (2020)

2020

Earlier work this paper cites.

Wu, J., Hu, W., Xiong, H., Huan, J., Braverman, V., Zhu, Z.: On the noisy gradient descent that generalizes as sgd. In: International Conference on Machine Learning. pp. 10367–10376. PMLR (2020)

2020

Earlier work this paper cites.

Zhang, L., Wang, X., Yang, D., Sanford, T., Harmon, S., Turkbey, B., Wood, B.J., Roth, H., Myronenko, A., Xu, D., et al.: Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE transactions on medical imaging 39

2020

Cited alongside, same era.

Zhou, P., Feng, J., Ma, C., Xiong, C., Hoi, S.C.H., et al.: Towards theoretically understanding why sgd generalizes better than adam in deep learning. Advances in Neural Information Processing Systems 33

2020

Cited alongside, same era.

Amir, I., Koren, T., Livni, R.: Sgd generalizes better than gd (and regularization doesn’t help). In: Conference on Learning Theory. pp. 63–92. PMLR (2021)

2021

Cited alongside, same era.

Foret, P., Kleiner, A., Mobahi, H., Neyshabur, B.: Sharpness-aware minimization for efficiently improving generalization. In: International Conference on Learning Representations (2021), https://openreview.net/forum?id=6Tm1mposlrM

2021

Cited alongside, same era.

Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Domain generalization with mixstyle. In: International Conference on Learning Representations (2021), https://openreview.net/forum?id=6xHJ37MVxxp

2021

Later among the works it cites.

Cardoso, M.J., Li, W., Brown, R., Ma, N., Kerfoot, E., Wang, Y., Murrey, B., Myronenko, A., Zhao, C., Yang, D., et al.: Monai: An open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701 (2022)

Original

2022

Later among the works it cites.

Chen, C., Li, Z., Ouyang, C., Sinclair, M., Bai, W., Rueckert, D.: Maxstyle: Adversarial style composition for robust medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part V. pp. 151–161. Springer (2022)

2022

Later among the works it cites.

Li, X., Dai, Y., Ge, Y., Liu, J., Shan, Y., DUAN, L.: Uncertainty modeling for out-of-distribution generalization. In: International Conference on Learning Representations (2022), https://openreview.net/forum?id=6HN7LHyzGgC