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People perceive the world with different senses, such as sight, hearing, smell, and touch.
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Choi, J.H., Lee, J.S.: Confidence-based deep multimodal fusion for activity recognition. In: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. pp. 1548–1556 (2018)
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2018
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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)
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Choi, J.H., Lee, J.S.: Embracenet: A robust deep learning architecture for multimodal classification. Information Fusion 51
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Choi, J.H., Lee, J.S.: Embracenet for activity: A deep multimodal fusion architecture for activity recognition. In: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers. pp. 693–698 (2019)
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Hu, M., Maillard, M., Zhang, Y., Ciceri, T., La Barbera, G., Bloch, I., Gori, P.: Knowledge distillation from multi-modal to mono-modal segmentation networks. In: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. pp. 772–781. Springer International Publishing, Cham (2020)
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Shen, L., Zhu, W., Wang, X., Xing, L., Pauly, J.M., Turkbey, B., Harmon, S.A., Sanford, T.H., Mehralivand, S., Choyke, P.L., Wood, B.J., Xu, D.: Multi-domain image completion for random missing input data. IEEE Transactions on Medical Imaging 40
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2019
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Dorent, R., Joutard, S., Modat, M., Ourselin, S., Vercauteren, T.: Hetero-modal variational encoder-decoder for joint modality completion and segmentation. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.T., Khan, A. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. pp. 74–82. Springer International Publishing, Cham (2019)
2019
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Guo, Z., Li, X., Huang, H., Guo, N., Li, Q.: Deep learning-based image segmentation on multimodal medical imaging. IEEE Transactions on Radiation and Plasma Medical Sciences 3
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2019
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Wang, L., Gjoreski, H., Ciliberto, M., Mekki, S., Valentin, S., Roggen, D.: Enabling reproducible research in sensor-based transportation mode recognition with the sussex-huawei dataset. IEEE Access 7
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Chen, C., Dou, Q., Jin, Y., Liu, Q., Heng, P.A.: Learning with privileged multimodal knowledge for unimodal segmentation. IEEE Transactions on Medical Imaging pp. 1–1 (2021). https://doi.org/10.1109/TMI.2021.3119385
2021
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Ouyang, J., Adeli, E., Pohl, K.M., Zhao, Q., Zaharchuk, G.: Representation disentanglement for multi-modal brain mri analysis. In: International Conference on Information Processing in Medical Imaging. pp. 321–333. Springer (2021)
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Wang, Y., Zhang, Y., Liu, Y., Lin, Z., Tian, J., Zhong, C., Shi, Z., Fan, J., He, Z.: Acn: Adversarial co-training network for brain tumor segmentation with missing modalities. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. pp. 410–420. Springer International Publishing, Cham (2021)
2021
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2021
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Yang, Q., Guo, X., Chen, Z., Woo, P.Y., Yuan, Y.: D2-net: Dual disentanglement network for brain tumor segmentation with missing modalities. IEEE Transactions on Medical Imaging (2022)
2022
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