Publisher
Springer Nature Switzerland
Reference26 articles.
1. Zhou, S., Nie, D., Adeli, E., Yin, J., Lian, J., Shen, D.: High-resolution encoder-decoder networks for low-contrast medical image segmentation. IEEE Trans. Image Process. 29, 461–475 (2019)
2. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, 5–9 October 2015, Proceedings, Part III 18, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
3. Hesamian, M., Jia, W., He, X., Kennedy, P.: Deep learning techniques for medical image segmentation: achievements and challenges. J. Digit. Imaging 32, 582–596 (2019)
4. Liu, L., Wolterink, J., Brune, C., Veldhuis, R.: Anatomy-aided deep learning for medical image segmentation: a review. Phys. Med. Biol. 66, 11TR01 (2021)
5. Yang, H., Wang, Z., Liu, X., Li, C., Xin, J., Wang, Z.: Deep learning in medical image super resolution: a review. In: Applied Intelligence, pp. 1–26 (2023). 10, 111-146 (2017)