A Gaussian Process Model Based Generative Framework for Data Augmentation of Multi-modal 3D Image Volumes-Reference-Cited by-同舟云学术

A Gaussian Process Model Based Generative Framework for Data Augmentation of Multi-modal 3D Image Volumes

Published:2020 Issue: Volume: Page:90-100
ISSN:0302-9743
Container-title:Simulation and Synthesis in Medical Imaging
language:
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Author:

Nbonsou Tegang Nicolas H.^ORCID,Fouefack Jean-Rassaire^ORCID,Borotikar Bhushan^ORCID,Burdin Valérie^ORCID,Douglas Tania S.^ORCID,Mutsvangwa Tinashe E. M.^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-59520-3_10

Reference19 articles.

1. Arora, S., Risteski, A., Zhang, Y.: Do GANs learn the distribution? Some theory and empirics. In: International Conference on Learning Representations (2018)

2. Bauer, D.F., et al.: Synthesis of CT images using CycleGANs: enhancement of anatomical accuracy. In: International Conference on Medical Imaging with Deep Learning. Proceedings of Machine Learning Research, vol. 102. PMLR (2019)

3. Burgos, N., et al.: Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE Trans. Med. Imaging 33(12), 2332–2341 (2014)

4. Lecture Notes in Computer Science;JP Cohen,2018

5. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

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