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Automatic parsing of anatomical objects in X-ray images is critical to many clinical applications in particular towards image-guided invention and workflow automation.
Dynamic layer separation for coronary dsa and enhancement in fluoroscopic sequences
Zhu, Y., Prummer, S., Wang, P., Chen, T., Comaniciu, D., Ostermeier, M.: · 2009
Earlier work this paper cites.
Conditional generative adversarial nets
Mirza, M., Osindero, S.: · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
Earlier work this paper cites.
Domain separation networks
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: · 2016
Cited alongside, same era.
Adversarial discriminative domain adaptation
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: · 2017
Cited alongside, same era.
Unsupervised pixel-level domain adaptation with generative adversarial networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: · 2017
Cited alongside, same era.
X-ray in-depth decomposition: Revealing the latent structures
Albarqouni, S., Fotouhi, J., Navab, N.:
Cited in the paper.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.:
Cited in the paper.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: · 2017
Later among the works it cites.
Densely connected convolutional networks
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: · 2017
Later among the works it cites.
The one hundred layers tiramisu
Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: · 2017
Later among the works it cites.
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