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In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H2NF-Net) to segment brain tumor in multimodal MR images.
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging 34(10), 1993–2024 (2014)
2014
Earlier work this paper cites.
Saxena, S., Verbeek, J.: Convolutional neural fabrics. In: Advances in Neural Information Processing Systems. pp. 4053–4061 (2016)
2016
Earlier work this paper cites.
Bakas, S., Akbari, H., Sotiras, A., et al.: Segmentation labels for the pre-operative scans of the tcga-gbm collection (2017)
2017
Earlier work this paper cites.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the tcga-lgg collection. The cancer imaging archive 286 (2017)
2017
Earlier work this paper cites.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific data 4, 170117 (2017)
2017
Earlier work this paper cites.
Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., 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 36, 61–78 (2017)
2017
Earlier work this paper cites.
Pohlen, T., Hermans, A., Mathias, M., Leibe, B.: Full-resolution residual networks for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4151–4160 (2017)
2017
Earlier work this paper cites.
Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Cardoso, M.J.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pp. 240–248. Springer (2017)
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net. In: International MICCAI Brainlesion Workshop. pp. 234–244. Springer (2018)
2018
Cited alongside, same era.
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: International MICCAI Brainlesion Workshop. pp. 311–320. Springer (2018)
2018
Zhao, H., Zhang, Y., Liu, S., Shi, J., Change Loy, C., Lin, D., Jia, J.: Psanet: Point-wise spatial attention network for scene parsing. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 267–283 (2018)
2018
Later among the works it cites.
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: Dual attention network for scene segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3146–3154 (2019)
2019
Later among the works it cites.
Jiang, Z., Ding, C., Liu, M., Tao, D.: Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task. In: International MICCAI Brainlesion Workshop. pp. 231–241. Springer (2019)
2019
Later among the works it cites.
Li, X., Zhong, Z., Wu, J., Yang, Y., Lin, Z., Liu, H.: Expectation-maximization attention networks for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 9167–9176 (2019)
2019
Later among the works it cites.
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Cited alongside, same era.
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7794–7803 (2018)
2018
Cited alongside, same era.
Wu, Y., Xia, Y., Song, Y., Zhang, Y., Cai, W.: Multiscale network followed network model for retinal vessel segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 119–126. Springer (2018)
2018
Cited alongside, same era.
2019
Later among the works it cites.
Jia, H., Xia, Y., Cai, W., Huang, H.: Learning high-resolution and efficient non-local features for brain glioma segmentation in mr images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 480–490. Springer (2020)
2020
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