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Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease.
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He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016)
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Kamnitsas, K., Ledig, C., andJoanna P. Simpson, V.F.N., Kane, A.D., Menon, D.K., Rueckert, D., , Glocker, B.: Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical Image Analysis (2016)
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Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: Fourth International Conference on 3D Vision (3DV) (2016)
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Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., John Freymann, K.F., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the tcga-gbm collection. The Cancer Imaging Archive (2017), https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
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Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., John Freymann, K.F., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the tcga-lgg collection. The Cancer Imaging Archive (2017), https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
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Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: International Conf. on Medical Image Computing and Computer Assisted Intervention. Multimodal Brain Tumor Segmentation Challenge (MICCAI, 2017). LNCS
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Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018). Multimodal Brain Tumor Segmentation Challenge (BraTS 2018). BrainLes 2018 workshop. LNCS, Springer (2018)
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2017
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Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 1800–1807 (2017)
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Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2261–2269 (2017)
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Kamnitsas, K., W. Bai, E.F., McDonagh, S., Sinclair, M., Pawlowski, N., Rajchl, M., Lee, M., Kainz, B., Rueckert, D., Glocker, B.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: International Conf. on Medical Image Computing and Computer Assisted Intervention. Multimodal Brain Tumor Segmentation Challenge (MICCAI, 2017). LNCS
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2018
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McKinley, R., Meier, R., Wiest, R.: Ensembles of densely-connected cnns with label-uncertainty for brain tumor segmentation. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018). Multimodal Brain Tumor Segmentation Challenge (BraTS 2018). BrainLes 2018 workshop. LNCS, Springer (2018)
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Wu, Y., He, K.: Group normalization. In: European Conference on Computer Vision (ECCV) (2018)
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Zhou, C., Chen, S., Ding, C., Tao, D.: Learning contextual and attentive information for brain tumor segmentation. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018). Multimodal Brain Tumor Segmentation Challenge (BraTS 2018). BrainLes 2018 workshop. LNCS, Springer (2018)
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