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The U-Net is arguably the most successful segmentation architecture in the medical domain.
Ficarra, V., Novara, G., Secco, S., Macchi, V., Porzionato, A., De Caro, R., Artibani, W.: Preoperative aspects and dimensions used for an anatomical (padua) classification of renal tumours in patients who are candidates for nephron-sparing surgery. European urology 56
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Yang, G., Gu, J., Chen, Y., Liu, W., Tang, L., Shu, H., Toumoulin, C.: Automatic kidney segmentation in ct images based on multi-atlas image registration. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 5538–5541. IEEE (2014)
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Landman, B., Xu, Z., Eugenio Igelsias, J., Styner, M., Langerak, T., Klein, A.: Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge (2015)
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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI. pp. 234–241. Springer (2015)
2015
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Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention. pp. 424–432. Springer (2016)
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Cited alongside, same era.
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European conference on computer vision. pp. 630–645. Springer (2016)
2016
Cited alongside, same era.
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: International Conference on 3D Vision (3DV). pp. 565–571. IEEE (2016)
2016
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Skalski, A., Jakubowski, J., Drewniak, T.: Kidney tumor segmentation and detection on computed tomography data. In: 2016 IEEE International Conference on Imaging Systems and Techniques (IST). pp. 238–242. IEEE (2016)
2016
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Taha, A., Lo, P., Li, J., Zhao, T.: Kid-net: convolution networks for kidney vessels segmentation from ct-volumes. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 463–471. Springer (2018)
2018
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2018
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2019
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2019
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Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch (2017)
2017
Cited alongside, same era.
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-denseunet: Hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE TMI 37
2018
Cited alongside, same era.
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2019
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2019
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