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Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI).
Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H., Hawk, S.T., Van Knippenberg, A.: Presentation and validation of the radboud faces database. Cognition and emotion 24
2010
Earlier work this paper cites.
Iglesias, J.E., Konukoglu, E., Zikic, D., Glocker, B., Van Leemput, K., Fischl, B.: Is synthesizing mri contrast useful for inter-modality analysis? In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 631–638. Springer (2013)
2013
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., Huisman, H.: Computer-aided detection of prostate cancer in mri. IEEE transactions on medical imaging 33
2014
Earlier work this paper cites.
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
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Van Nguyen, H., Zhou, K., Vemulapalli, R.: Cross-domain synthesis of medical images using efficient location-sensitive deep network. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 677–684. Springer (2015)
2015
Earlier work this paper cites.
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: A benchmark. In: Proceedings of the IEEE international conference on computer vision. pp. 1116–1124 (2015)
2015
Earlier work this paper cites.
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In: Advances in neural information processing systems. pp. 2172–2180 (2016)
2016
Earlier work this paper cites.
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
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.
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
2017
Earlier work this paper cites.
Chartsias, A., Joyce, T., Giuffrida, M.V., Tsaftaris, S.A.: Multimodal mr synthesis via modality-invariant latent representation. IEEE transactions on medical imaging 37
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., Lerchner, A.: beta-vae: Learning basic visual concepts with a constrained variational framework. ICLR 2
2017
Cited alongside, same era.
Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1501–1510 (2017)
2017
Cited alongside, same era.
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1125–1134 (2017)
2017
Cited alongside, same era.
Kamnitsas, K., Baumgartner, C., Ledig, C., Newcombe, V., Simpson, J., Kane, A., Menon, D., Nori, A., Criminisi, A., Rueckert, D., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: International conference on information processing in medical imaging. pp. 597–609. Springer (2017)
Huo, Y., Xu, Z., Bao, S., Assad, A., Abramson, R.G., Landman, B.A.: Adversarial synthesis learning enables segmentation without target modality ground truth. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). pp. 1217–1220. IEEE (2018)
2018
Later among the works it cites.
Lee, H.Y., Tseng, H.Y., Huang, J.B., Singh, M.K., Yang, M.H.: Diverse image-to-image translation via disentangled representations. In: European Conference on Computer Vision (2018)
2018
Later among the works it cites.
Liu, A.H., Liu, Y.C., Yeh, Y.Y., Wang, Y.C.F.: A unified feature disentangler for multi-domain image translation and manipulation. In: Advances in Neural Information Processing Systems. pp. 2590–2599 (2018)
2018
Later among the works it cites.
Liu, Y.C., Yeh, Y.Y., Fu, T.C., Wang, S.D., Chiu, W.C., Frank Wang, Y.C.: Detach and adapt: Learning cross-domain disentangled deep representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8867–8876 (2018)
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2017
Cited alongside, same era.
Kim, T., Cha, M., Kim, H., Lee, J.K., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. pp. 1857–1865. JMLR. org (2017)
2017
Cited alongside, same era.
Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in neural information processing systems. pp. 700–708 (2017)
2017
Cited alongside, same era.
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2794–2802 (2017)
2017
Cited alongside, same era.
Nie, D., Trullo, R., Lian, J., Petitjean, C., Ruan, S., Wang, Q., Shen, D.: Medical image synthesis with context-aware generative adversarial networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 417–425. Springer (2017)
2017
Cited alongside, same era.
Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3d fully convolutional deep networks. In: International Workshop on Machine Learning in Medical Imaging. pp. 379–387. Springer (2017)
2017
Cited alongside, same era.
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2107–2116 (2017)
2017
Cited alongside, same era.
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6924–6932 (2017)
2017
Cited alongside, same era.
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision. pp. 2223–2232 (2017)
2017
Cited alongside, same era.
2018
Later among the works it cites.
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8798–8807 (2018)
2018
Later among the works it cites.
2018
Later among the works it cites.
Zhang, Z., Yang, L., Zheng, Y.: Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 9242–9251 (2018)
2018
Later among the works it cites.
Zhu, W., Xiang, X., Tran, T.D., Hager, G.D., Xie, X.: Adversarial deep structured nets for mass segmentation from mammograms. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). pp. 847–850. IEEE (2018)
2018
Later among the works it cites.
Dar, S.U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., Çukur, T.: Image synthesis in multi-contrast mri with conditional generative adversarial networks. IEEE transactions on medical imaging (2019)
2019
Later among the works it cites.
Lee, D., Kim, J., Moon, W.J., Ye, J.C.: Collagan: Collaborative gan for missing image data imputation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2487–2496 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
Lin, J., Chen, Z., Xia, Y., Liu, S., Qin, T., Luo, J.: Exploring explicit domain supervision for latent space disentanglement in unpaired image-to-image translation. IEEE transactions on pattern analysis and machine intelligence (2019)
2019
Later among the works it cites.
Sharma, A., Hamarneh, G.: Missing mri pulse sequence synthesis using multi-modal generative adversarial network. IEEE transactions on medical imaging (2019)
2019
Later among the works it cites.
Zheng, Z., Yang, X., Yu, Z., Zheng, L., Yang, Y., Kautz, J.: Joint discriminative and generative learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2138–2147 (2019)
2019
Later among the works it cites.