Fetching the paper…
Reading the bibliography…
We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume.
Axel, L., Summers, R., Kressel, H., Charles, C.: Respiratory effects in two-dimensional fourier transform mr imaging. Radiology 160
1986
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
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)
2016
Earlier work this paper cites.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40
2017
Earlier work this paper cites.
Jin, K.H., Um, J.Y., Lee, D., Lee, J., Park, S.H., Ye, J.C.: Mri artifact correction using sparse+ low-rank decomposition of annihilating filter-based hankel matrix. Magnetic resonance in medicine 78
2017
Earlier work this paper cites.
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch (2017)
2017
Earlier work this paper cites.
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)
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Shaw, R., Sudre, C., Ourselin, S., Cardoso, M.J.: Mri k-space motion artefact augmentation: model robustness and task-specific uncertainty. In: International Conference on Medical Imaging with Deep Learning–Full Paper Track (2018)
2018
Earlier work this paper cites.
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)
2018
Earlier work this paper cites.
Hu, H., Zhang, Z., Xie, Z., Lin, S.: Local relation networks for image recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3464–3473 (2019)
2019
Earlier work this paper cites.
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
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Closest in time.
2021
Closest in time.
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning. pp. 10347–10357. PMLR (2021)
2021
Closest in time.
Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: Transbts: Multimodal brain tumor segmentation using transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 109–119. Springer (2021)
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Isensee, F., Maier-Hein, K.H.: nnu-net for brain tumor segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II. vol. 12658, p. 118. Springer Nature (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Wang, Z., Liu, J.C.: Translating math formula images to latex sequences using deep neural networks with sequence-level training. International Journal on Document Analysis and Recognition (IJDAR) 24
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6881–6890 (2021)
2021
Closest in time.
2021
Closest in time.
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 574–584 (2022)
2022
Closest in time.