book chapter · Lecture notes in computer science · 2018
Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks
Hoo-Chang Shin, Neil Tenenholtz, Jameson K. Rogers, Christopher G. Schwarz, Matthew L. Senjem, Jeffrey L. Gunter, Katherine P. Andriole, Mark Michalski
Cited by 607Abstract and details only
Abstract
The index has no abstract for this paper. The publisher’s page may have one.
Cite this paper
Shin, H.-C., Tenenholtz, N., Rogers, J. K., Schwarz, C. G., Senjem, M. L., Gunter, J. L., Andriole, K. P., & Michalski, M. (2018). Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks. Lecture notes in computer science, 1–11. https://doi.org/10.1007/978-3-030-00536-8_1
Shin, Hoo-Chang, et al. “Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks.” Lecture notes in computer science, 2018, pp. 1–11, https://doi.org/10.1007/978-3-030-00536-8_1.
Shin, Hoo-Chang, Neil Tenenholtz, Jameson K. Rogers, Christopher G. Schwarz, Matthew L. Senjem, Jeffrey L. Gunter, Katherine P. Andriole, and Mark Michalski. 2018. “Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks.” Lecture notes in computer science: 1–11. https://doi.org/10.1007/978-3-030-00536-8_1.
Shin, H.-C. et al. (2018) ‘Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks’, Lecture notes in computer science, pp. 1–11. Available at: https://doi.org/10.1007/978-3-030-00536-8_1.
@incollection{shin2018medical,
title = {{Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks}},
author = {Shin, Hoo-Chang and Tenenholtz, Neil and Rogers, Jameson K. and Schwarz, Christopher G. and Senjem, Matthew L. and Gunter, Jeffrey L. and Andriole, Katherine P. and Michalski, Mark},
booktitle = {Lecture notes in computer science},
pages = {1--11},
year = {2018},
doi = {10.1007/978-3-030-00536-8_1}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.
Related papers
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
Eric Ryan Chan, Marco Monteiro, Petr Kellnhofer et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2021
Cited by 730Open access
Generation and Study of the Synthetic Brain Electron Microscopy Dataset for Segmentation Purpose
N.A. Sokolov, Evgeny Vasiliev, Alexandra Getmanskaya · International Conference on Computer Graphics and Vision (GraphiCon) · 2022
Cited by 0Open access
ACGAN: Attribute controllable person image synthesis GAN for pose transfer
Shaoyue Lin, YanJun Zhang · Journal of Visual Communication and Image Representation · 2022
Cited by 2
PBGN: Phased Bidirectional Generation Network in Text-to-Image Synthesis
Jianwei Zhu, Zhixin Li, Jiahui Wei et al. · Neural Processing Letters · 2022
Cited by 11
Synthetic data, real errors: how (not) to publish and use synthetic data
Boris van Breugel, Zhaozhi Qian, Mihaela van der Schaar · arXiv (Cornell University) · 2023
Cited by 26Open access
Empirical Evaluation on Synthetic Data Generation with Generative Adversarial Network
Pei-Hsuan Lu, Pang-Chieh Wang, Chia-Mu Yu · International Conference on Web Intelligence, Mining and Semantics · 2019
Cited by 44