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Training deep convolutional neural networks usually requires a large amount of labeled data.
Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. arXiv preprint (2016)
2016
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Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 3DV. pp. 565–571 (2016)
2016
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Bai, W., Oktay, O., Sinclair, M.e.a.: Semi-supervised learning for network-based cardiac mr image segmentation. In: MICCAI. pp. 253–260 (2017)
2017
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Baur, C., Albarqouni, S., Navab, N.: Semi-supervised deep learning for fully convolutional networks. In: MICCAI. pp. 311–319 (2017)
2017
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Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? In: NIPS. pp. 5574–5584 (2017)
2017
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Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: NIPS (2017)
2017
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Yang, X., Bian, C., Yu, L., Ni, D., Heng, P.A.: Hybrid loss guided convolutional networks for whole heart parsing. In: International Workshop on STACOM (2017)
2017
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Yu, L., Cheng, J.Z., Dou, Q., Yang, X., Chen, H., Qin, J., Heng, P.A.: Automatic 3d cardiovascular mr segmentation with densely-connected volumetric convnets. In: MICCAI. pp. 287–295. Springer (2017)
2017
Cited alongside, same era.
Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z.: Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: MICCAI. pp. 408–416 (2017)
2017
Cited alongside, same era.
Chartsias, A., Joyce, T., Papanastasiou, G., Semple, S., Williams, M., Newby, D., Dharmakumar, R., Tsaftaris, S.A.: Factorised spatial representation learning: application in semi-supervised myocardial segmentation. MICCAI pp. 490–498 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Li, X., Yu, L., Chen, H., Fu, C.W., Heng, P.A.: Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model. BMVC (2018)
2018
Later among the works it cites.
Nie, D., Gao, Y., Wang, L., Shen, D.: Asdnet: Attention based semi-supervised deep networks for medical image segmentation. In: MICCAI. pp. 370–378 (2018)
2018
Later among the works it cites.
Perone, C.S., Cohen-Adad, J.: Deep semi-supervised segmentation with weight-averaged consistency targets. In: DLMIA workshop (2018)
2018
Later among the works it cites.
2018
Later among the works it cites.
Cui, W., Liu, Y., Li, Y., Guo, M., Li, Y., Li, X., Wang, T., Zeng, X., Ye, C.: Semi-supervised brain lesion segmentation with an adapted mean teacher model. In: IPMI. pp. 554–565 (2019)
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Dong, N., Kampffmeyer, M., Liang, X., Wang, Z., Dai, W., Xing, E.: Unsupervised domain adaptation for automatic estimation of cardiothoracic ratio. In: MICCAI. pp. 544–552 (2018)
2018
Cited alongside, same era.
Ganaye, P.A., Sdika, M., Benoit-Cattin, H.: Semi-supervised learning for segmentation under semantic constraint. In: MICCAI. pp. 595–602 (2018)
2018
Cited alongside, same era.
2019
Closest in time.
Xiong, Z., Fedorov, V.V., Fu, X., Cheng, E., Macleod, R., Zhao, J.: Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network. TMI 38
2019
Closest in time.