Retinal image synthesis from multiple-landmarks input with generative adversarial networks

Yu Z, Xiang Q, Meng J, Kou C, Ren Q, Lu Y (2019)

Publication Type: Journal article

Publication year: 2019

Journal

Biomedical Engineering Online BioMed Central

Book Volume: 18

DOI: 10.1186/s12938-019-0682-x

Abstract

Background: Medical datasets, especially medical images, are often imbalanced due to the different incidences of various diseases. To address this problem, many methods have been proposed to synthesize medical images using generative adversarial networks (GANs) to enlarge training datasets for facilitating medical image analysis. For instance, conventional methods such as image-to-image translation techniques are used to synthesize fundus images with their respective vessel trees in the field of fundus image.

Authors with CRIS profile

Yanye Lu Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)

Involved external institutions

Peking University (PKU) / 北京大学 CN China (CN) Beijing University of Posts and Telecommunications (BUPT) / 北京邮电大学 CN China (CN)

How to cite

APA:

Yu, Z., Xiang, Q., Meng, J., Kou, C., Ren, Q., & Lu, Y. (2019). Retinal image synthesis from multiple-landmarks input with generative adversarial networks. Biomedical Engineering Online, 18. https://dx.doi.org/10.1186/s12938-019-0682-x

MLA:

Yu, Zekuan, et al. "Retinal image synthesis from multiple-landmarks input with generative adversarial networks." Biomedical Engineering Online 18 (2019).

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