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UNet and its variants have been widely used in medical image segmentation.
Kalman, R.E.: A new approach to linear filtering and prediction problems (1960)
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Jaeger, S., Candemir, S., Antani, S., Wáng, Y.X.J., Lu, P.X., Thoma, G.: Two public chest x-ray datasets for computer-aided screening of pulmonary diseases. Quantitative imaging in medicine and surgery 4
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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234–241. Springer (2015)
2015
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
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Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2881–2890 (2017)
2017
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Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., Zhang, T., Gao, S., Liu, J.: Ce-net: Context encoder network for 2d medical image segmentation. IEEE transactions on medical imaging 38
2019
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Myronenko, A.: 3d mri brain tumor segmentation using autoencoder regularization. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4. pp. 311–320. Springer (2019)
2019
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Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., Rueckert, D.: Attention gated networks: Learning to leverage salient regions in medical images. Medical image analysis 53
2019
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Shazeer, N.: Glu variants improve transformer. arXiv preprint arXiv:2002.05202 (2020)
2020
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2021
Cited alongside, same era.
Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop. pp. 272–284. Springer (2021)
2021
Cited alongside, same era.
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18
2021
Cited alongside, same era.
Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., Jégou, H.: Going deeper with image transformers. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 32–42 (2021)
2021
Cited alongside, same era.
2023
Later among the works it cites.
Li, Y., Fan, Y., Xiang, X., Demandolx, D., Ranjan, R., Timofte, R., Van Gool, L.: Efficient and explicit modelling of image hierarchies for image restoration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18278–18289 (2023)
2023
Later among the works it cites.
Ruan, J., Xie, M., Gao, J., Liu, T., Fu, Y.: Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 481–490. Springer (2023)
2023
Later among the works it cites.
Chen, Z., Zhang, Y., Gu, J., Kong, L., Yang, X.: Recursive generalization transformer for image super-resolution. In: Proceedings of the International conference on learning representations (2024)
2024
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Zhang, Y., Peng, C., Peng, L., Huang, H., Tong, R., Lin, L., Li, J., Chen, Y.W., Chen, Q., Hu, H., et al.: Multi-phase liver tumor segmentation with spatial aggregation and uncertain region inpainting. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24. pp. 68–77. Springer (2021)
2021
Cited alongside, same era.
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 574–584 (2022)
2022
Cited alongside, same era.
Liao, W., Xiong, H., Wang, Q., Mo, Y., Li, X., Liu, Y., Chen, Z., Huang, S., Dou, D.: Muscle: Multi-task self-supervised continual learning to pre-train deep models for x-ray images of multiple body parts. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 151–161. Springer (2022)
2022
Cited alongside, same era.
Bilic, P., Christ, P., Li, H.B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G.E.H., Chartrand, G., et al.: The liver tumor segmentation benchmark (lits). Medical Image Analysis 84
2023
Cited alongside, same era.
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2024
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2024
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