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Medical image segmentation is one of the most fundamental tasks concerning medical information analysis.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86
1998
Earlier work this paper cites.
Chowdhury, G.G.: Natural language processing. Annual review of information science and technology 37
2003
Earlier work this paper cites.
2014
Earlier work this paper cites.
Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al.: Evaluation of prostate segmentation algorithms for mri: the promise12 challenge. Medical image analysis 18
2014
Earlier work this paper cites.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3431–3440 (2015)
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
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
Earlier work this paper cites.
2016
Earlier work this paper cites.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2921–2929 (2016)
2016
Earlier work this paper cites.
Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 11–19 (2017)
2017
Cited alongside, same era.
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
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp. 3–19 (2018)
2018
Cited alongside, same era.
2020
Later among the works it cites.
2021
Later among the works it cites.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10012–10022 (2021)
2021
Later among the works it cites.
Tomar, N.K., Jha, D., Ali, S., Johansen, H.D., Johansen, D., Riegler, M.A., Halvorsen, P.: Ddanet: Dual decoder attention network for automatic polyp segmentation. In: International Conference on Pattern Recognition. pp. 307–314. Springer (2021)
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Xiao, X., Lian, S., Luo, Z., Li, S.: Weighted res-unet for high-quality retina vessel segmentation. In: 2018 9th international conference on information technology in medicine and education (ITME). pp. 327–331. IEEE (2018)
2018
Cited alongside, same era.
Zhang, R., Zhao, L., Lou, W., Abrigo, J.M., Mok, V.C., Chu, W.C., Wang, D., Shi, L.: Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37
2018
Cited alongside, same era.
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pp. 3–11. Springer (2018)
2018
Cited alongside, same era.
Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T.: Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 338
2019
Cited alongside, same era.
Carion, N., et al.: End-to-end object detection with transformers. In: European Conference on Computer Vision. pp. 213–229. Springer (2020)
2020
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6881–6890 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.