Generative Adversarial Networks and Its Applications in Biomedical Informatics
The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Among them, the generator and discriminator are implicit function expressions, usually implemented by deep neural networks. GAN can learn the generative model of any data distribution...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-05-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fpubh.2020.00164/full |
| _version_ | 1824008443832303616 |
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| author | Lan Lan Lei You Zeyang Zhang Zhiwei Fan Weiling Zhao Nianyin Zeng Yidong Chen Xiaobo Zhou |
| author_facet | Lan Lan Lei You Zeyang Zhang Zhiwei Fan Weiling Zhao Nianyin Zeng Yidong Chen Xiaobo Zhou |
| author_sort | Lan Lan |
| collection | DOAJ |
| description | The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Among them, the generator and discriminator are implicit function expressions, usually implemented by deep neural networks. GAN can learn the generative model of any data distribution through adversarial methods with excellent performance. It has been widely applied to different areas since it was proposed in 2014. In this review, we introduced the origin, specific working principle, and development history of GAN, various applications of GAN in digital image processing, Cycle-GAN, and its application in medical imaging analysis, as well as the latest applications of GAN in medical informatics and bioinformatics. |
| first_indexed | 2024-12-18T20:57:19Z |
| format | Article |
| id | doaj.art-d42e311451e143d3a3b17031c0002500 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-12-18T20:57:19Z |
| publishDate | 2020-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-d42e311451e143d3a3b17031c00025002022-12-21T20:53:12ZengFrontiers Media S.A.Frontiers in Public Health2296-25652020-05-01810.3389/fpubh.2020.00164523500Generative Adversarial Networks and Its Applications in Biomedical InformaticsLan Lan0Lei You1Zeyang Zhang2Zhiwei Fan3Weiling Zhao4Nianyin Zeng5Yidong Chen6Xiaobo Zhou7West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, ChinaCenter for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United StatesDepartment of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, ChinaDepartment of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, ChinaCenter for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United StatesDepartment of Instrumental and Electrical Engineering, Xiamen University, Fujian, ChinaDepartment of Computer Science and Technology, College of Computer Science, Sichuan University, Chengdu, ChinaCenter for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United StatesThe basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Among them, the generator and discriminator are implicit function expressions, usually implemented by deep neural networks. GAN can learn the generative model of any data distribution through adversarial methods with excellent performance. It has been widely applied to different areas since it was proposed in 2014. In this review, we introduced the origin, specific working principle, and development history of GAN, various applications of GAN in digital image processing, Cycle-GAN, and its application in medical imaging analysis, as well as the latest applications of GAN in medical informatics and bioinformatics.https://www.frontiersin.org/article/10.3389/fpubh.2020.00164/fullGenerative Adversarial Networks (GAN)generatordiscriminatordata augmentationimage conversionbiomedical applications |
| spellingShingle | Lan Lan Lei You Zeyang Zhang Zhiwei Fan Weiling Zhao Nianyin Zeng Yidong Chen Xiaobo Zhou Generative Adversarial Networks and Its Applications in Biomedical Informatics Frontiers in Public Health Generative Adversarial Networks (GAN) generator discriminator data augmentation image conversion biomedical applications |
| title | Generative Adversarial Networks and Its Applications in Biomedical Informatics |
| title_full | Generative Adversarial Networks and Its Applications in Biomedical Informatics |
| title_fullStr | Generative Adversarial Networks and Its Applications in Biomedical Informatics |
| title_full_unstemmed | Generative Adversarial Networks and Its Applications in Biomedical Informatics |
| title_short | Generative Adversarial Networks and Its Applications in Biomedical Informatics |
| title_sort | generative adversarial networks and its applications in biomedical informatics |
| topic | Generative Adversarial Networks (GAN) generator discriminator data augmentation image conversion biomedical applications |
| url | https://www.frontiersin.org/article/10.3389/fpubh.2020.00164/full |
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