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Transformer, which can benefit from global (long-range) information modeling using self-attention mechanisms, has been successful in natural language processing and 2D image classification recently.
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Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3431–3440 (2015)
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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)
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Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention. pp. 424–432. Springer (2016)
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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)
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
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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)
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Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual u-net. IEEE Geoscience and Remote Sensing Letters 15
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Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., Rueckert, D.: Attention gated networks: Learning to leverage salient regions in medical images. Medical image analysis 53
2019
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Wang, F., Jiang, R., Zheng, L., Meng, C., Biswal, B.: 3d u-net based brain tumor segmentation and survival days prediction. In: International MICCAI Brainlesion Workshop. pp. 131–141. Springer (2019)
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Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision. pp. 213–229. Springer (2020)
2020
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2020
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Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pp. 3–11. Springer (2018)
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Frey, M., Nau, M.: Memory efficient brain tumor segmentation using an autoencoder-regularized u-net. In: International MICCAI Brainlesion Workshop. pp. 388–396. Springer (2019)
2019
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Li, X., Luo, G., Wang, K.: Multi-step cascaded networks for brain tumor segmentation. In: International MICCAI Brainlesion Workshop. pp. 163–173. Springer (2019)
2019
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Myronenko, A., Hatamizadeh, A.: Robust semantic segmentation of brain tumor regions from 3d mris. In: International MICCAI Brainlesion Workshop. pp. 82–89. Springer (2019)
2019
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2020
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2020
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2021
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