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DMNet for Semi-supervised Segmentation 541 5.Chartsias,A.,et al.:Factorised spatial representation learning:application in semi- supervised myocardial segmentation.In:Frangi,A.F.,Schnabel,J.A.,Davatzikos, C.,Alberola-Lopez,C.,Fichtinger,G.(eds.)MICCAI 2018.LNCS,vol.11071,pp. 490-498.Springer,.Cham(2018).https://doi.org/10.1007/978-3-030-00934-2.55 6.Chen,L.,Papandreou,G.,Kokkinos,I.,Murphy,K.,Yuille,A.L.:Semantic image segmentation with deep convolutional nets and fully connected CRFs.In:Proceed- ings of International Conference on Learning Representations (ICLR)(2015) 7.Chen,L.,Zhu,Y.,Papandreou,G.,Schroff,F.,Adam,H.:Encoder-decoder with atrous separable convolution for semantic image segmentation.In:Proceedings of European Conference on Computer Vision (ECCV)(2018) 8.Goodfellow,I.J.,et al.:Generative adversarial nets.In:Proceedings of Neural Information Processing Systems (NIPS)(2014) 9.Han,B.,Yao,Q.,Yu,X.,Niu,G.,Xu,M.,Hu,W.,Tsang,I.W.,Sugiyama,M.: Co-teaching:robust training of deep neural networks with extremely noisy labels. 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(eds.)MICCAI 2015.LNCS,vol.9351,pp.234-241.Springer,Cham (2015). https://doi.org/10.1007/978-3-31924574-428 17.Souly,N.,Spampinato,C.,Shah,M.:Semi and weakly supervised semantic seg- mentation using generative adversarial network.CoRR (2017) 18.Zhou,Y.,et al.:Collaborative learning of semi-supervised segmentation and classi- fication for medical images.In:Proceeding of Computer Vision and Pattern Recog- nition (CVPR)(2019)DMNet for Semi-supervised Segmentation 541 5. Chartsias, A., et al.: Factorised spatial representation learning: application in semi￾supervised myocardial segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L´opez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 490–498. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2 55 6. Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. 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Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic seg￾mentation. In: Proceeding of Computer Vision and Pattern Recognition (CVPR) (2015) 13. Milletari, F., Navab, N., Ahmadi, S.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceeding of 3D Vision (3DV) (2016) 14. Nie, D., Gao, Y., Wang, L., Shen, D.: ASDNet: attention based semi-supervised deep networks for medical image segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L´opez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 370–378. Springer, Cham (2018). https://doi.org/10.1007/978-3- 030-00937-3 43 15. Qiao, S., Shen, W., Zhang, Z., Wang, B., Yuille, A.L.: Deep co-training for semi￾supervised image recognition. In: Proceedings of European Conference on Com￾puter Vision (ECCV) (2018) 16. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomed￾ical image segmentation. 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