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End-to-end medical image segmentation is of great value for computer-aided diagnosis dominated by task-specific models, usually suffering from poor generalization.
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Pedraza, L., Vargas, C., Narváez, F., Durán, O., Muñoz, E., Romero, E.: An open access thyroid ultrasound image database. In: 10th International Symposium on Medical Information Processing and Analysis, pp. 188–193 (2015)
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Wunderling, T., Golla, B., Poudel, P., Arens, C., Friebe, M., Hansen, C.: Comparison of thyroid segmentation techniques for 3D ultrasound. In: Image Processing 2017, pp. 346–352 (2017)
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Leclerc, S., et al.: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans. Med. Imag. 38
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Gu, Z., et al.: Ce-net: Context encoder network for 2d medical image segmentation. IEEE Trans. Med. Imag. 38
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Yap, M. H., et al.: Breast ultrasound region of interest detection and lesion localisation. Artif. Intell. in Med. 107
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Kiranyaz, S., et al.: Left ventricular wall motion estimation by active polynomials for acute myocardial infarction detection. IEEE Access. 8
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Feng, S., et al.: CPFNet: Context pyramid fusion network for medical image segmentation. IEEE Trans. Med. Imag. 39
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Gu, R., et al.: CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Trans. Med. Imag. 40
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Liu, X., Song, L., Liu, S., Zhang, Y.: A review of deep-learning-based medical image segmentation methods. Sustainability. 13
2021
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Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6881–6890 (2021)
2021
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2021
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Zhang, Y., Liu, H., Hu, Q.: Transfuse: Fusing transformers and cnns for medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021, LNCS, vol. 12901, pp. 14–24. Springer, Cham (2021). \doi
2021
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Huang, X., Deng, Z., Li, D., Yuan, X., Fu, Y.: MISSFormer: An effective transformer for 2d medical image segmentation. IEEE Trans. Med. Imag. 42
2022
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2023
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2023
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2023
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Cao, H., et al. Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022)
2022
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Wu, H., Chen, S., Chen, G., Wang, W., Lei, B., Wen, Z.: FAT-Net: Feature adaptive transformers for automated skin lesion segmentation: Medical Image Anal. 76
2022
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Kirillov, A., et al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)
2023
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2023
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2023
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2023
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2023
Cited alongside, same era.
Gong, H., Chen, J., Chen, G., Li, H., Li, G., Chen, F.: Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules. Comput. Biol. Med. 155
2023
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Chen, G., Li, L., Dai, Y., Zhang, J., Yap, M. H.: AAU-net: an adaptive attention U-net for breast lesions segmentation in ultrasound images. IEEE Trans. Med. Imag. 42
2023
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He, A., Wang, K., Li, T., Du, C., Xia, S., Fu, H.: H2former: An efficient hierarchical hybrid transformer for medical image segmentation. IEEE Trans. Med. Imag. 42
2023
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Huang, Y., et al.: Segment anything model for medical images? Med. Image Anal. 92
2024
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Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15
2024
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2024
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