Semi-supervised Abdominal Multi-organ Segmentation via Contour Aware Dual-Task Consistency
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5597-4_21
Reference21 articles.
1. Jiao, R., Zhang, Y., Ding, L., et al.: Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation. Computers in Biology and Medicine 107840 (2023)
2. Luo, X., Chen, J., Song, T., Wang, G.: Semi-supervised medical image segmentation through dual-task consistency. In: Proceedings of the AAAI conference on artificial intelligence, pp. 8801–8809 (2021)
3. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241. Springer (2015)
4. Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), pp. 565–571. Ieee (2016)
5. Gibson, E., Giganti, F., Hu, Y., et al.: Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans. Med. Imaging 37, 1822–1834 (2018)
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