mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

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

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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)

2015

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Tulder, G.v., Bruijne, M.d.: Why does synthesized data improve multi-sequence classification? In: International Conference on Medical Image Computing and Computer Assisted Intervention. pp. 531–538. Springer (2015)

2015

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Havaei, M., Guizard, N., Chapados, N., Bengio, Y.: HeMIS: Hetero-modal image segmentation. In: International Conference on Medical Image Computing and Computer Assisted Intervention. pp. 469–477. Springer (2016)

2016

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Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 (2016)

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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. pp. 565–571. IEEE (2016)

2016

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Dou, Q., Yu, L., Chen, H., Jin, Y., Yang, X., Qin, J., Heng, P.A.: 3D deeply supervised network for automated segmentation of volumetric medical images. Medical Image Analysis 41

2017

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Tseng, K.L., Lin, Y.L., Hsu, W., Huang, C.Y.: Joint sequence learning and cross-modality convolution for 3D biomedical segmentation. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 6393–6400 (2017)

2017

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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. Advances in Neural Information Processing Systems 30

2017

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Zhou, C., Ding, C., Lu, Z., Wang, X., Tao, D.: One-pass multi-task convolutional neural networks for efficient brain tumor segmentation. In: International Conference on Medical Image Computing and Computer Assisted Intervention. pp. 637–645. Springer (2018)

2018

Cited alongside, same era.

Chen, C., Dou, Q., Jin, Y., Chen, H., Qin, J., Heng, P.A.: Robust multimodal brain tumor segmentation via feature disentanglement and gated fusion. In: International Conference on Medical Image Computing and Computer Assisted Intervention. pp. 447–456. Springer (2019)

Chen, C., Dou, Q., Jin, Y., Liu, Q., Heng, P.A.: Learning with privileged multimodal knowledge for unimodal segmentation. IEEE Transactions on Medical Imaging (2021)

2021

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Ding, Y., Yu, X., Yang, Y.: RFNet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3975–3984 (2021)

2021

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Peiris, H., Hayat, M., Chen, Z., Egan, G., Harandi, M.: A volumetric transformer for accurate 3D tumor segmentation. arXiv preprint arXiv:2111.13300 (2021)

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2021

Later among the works it cites.

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

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