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Currently, convolutional neural networks (CNN) (e.g., U-Net) have become the de facto standard and attained immense success in medical image segmentation.
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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)
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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)
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Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al.: Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). pp. 168–172. IEEE (2018)
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Gupta, A., Mallick, P., Sharma, O., Gupta, R., Duggal, R.: Pcseg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma. PloS one 13
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Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7132–7141 (2018)
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Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7794–7803 (2018)
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Azad, R., Asadi-Aghbolaghi, M., Fathy, M., Escalera, S.: Bi-directional convlstm u-net with densely connected convolutions. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). pp. 406–415 (2019). https://doi.org/10.1109/ICCVW.2019.00052
2019
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2019
Cited alongside, same era.
2020
Cited alongside, same era.
Cai, S., Tian, Y., Lui, H., Zeng, H., Wu, Y., Chen, G.: Dense-unet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network. Quantitative imaging in medicine and surgery 10
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Azad, R., Bozorgpour, A., Asadi-Aghbolaghi, M., Merhof, D., Escalera, S.: Deep frequency re-calibration u-net for medical image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3274–3283 (2021)
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Azad, R., Khosravi, N., Merhof, D.: Smu-net: Style matching u-net for brain tumor segmentation with missing modalities (2021)
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2021
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Chen, C.F.R., Fan, Q., Panda, R.: Crossvit: Cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 357–366 (2021)
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Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.W., Wu, J.: Unet 3+: A full-scale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1055–1059. IEEE (2020)
2020
Cited alongside, same era.
Lei, B., Xia, Z., Jiang, F., Jiang, X., Ge, Z., Xu, Y., Qin, J., Chen, S., Wang, T., Wang, S.: Skin lesion segmentation via generative adversarial networks with dual discriminators. Medical Image Analysis 64
2020
Cited alongside, same era.
Sinha, A., Dolz, J.: Multi-scale self-guided attention for medical image segmentation. IEEE journal of biomedical and health informatics 25
2020
Cited alongside, same era.
Valanarasu, J.M.J., Sindagi, V.A., Hacihaliloglu, I., Patel, V.M.: Kiu-net: Towards accurate segmentation of biomedical images using over-complete representations. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 363–373. Springer (2020)
2020
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2021
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Li, M., Lian, F., Wang, C., Guo, S.: Accurate pancreas segmentation using multi-level pyramidal pooling residual u-net with adversarial mechanism. BMC Medical Imaging 21
2021
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Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: Gated axial-attention for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 36–46. Springer (2021)
2021
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Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H., 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
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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
Closest in time.
Wu, H., Chen, S., Chen, G., Wang, W., Lei, B., Wen, Z.: Fat-net: Feature adaptive transformers for automated skin lesion segmentation. Medical Image Analysis 76
2022
Closest in time.