DiamondGAN: Unified Multi-modal Generative Adversarial Networks for MRI Sequences Synthesis-Reference-Cited by-同舟云学术

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DiamondGAN: Unified Multi-modal Generative Adversarial Networks for MRI Sequences Synthesis

Published:2019 Issue: Volume: Page:795-803
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Li Hongwei,Paetzold Johannes C.,Sekuboyina Anjany,Kofler Florian,Zhang Jianguo,Kirschke Jan S.,Wiestler Benedikt,Menze Bjoern

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-32251-9_87

Reference14 articles.

1. Borji, A.: Pros and cons of gan evaluation measures. Comput. Vis. Image Underst. 179, 41–65 (2019)

2. Choi, Y., et al.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: CVPR, pp. 8789–8797 (2018)

3. Dar, S.U., et al.: Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans. Med. Imaging (2019)

4. He, K., et al.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

5. Isola, P., et al.: Image-to-image translation with conditional adversarial networks. In: CVPR, pp. 1125–1134 (2017)

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