Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience
The fields of medicine and neuroscience often face challenges in obtaining a sufficient amount of diverse data for training machine learning models. Data augmentation can alleviate this issue by artificially synthesizing new data from existing data. Generative adversarial networks (GANs) provide a p...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2023.1162760/full |
| _version_ | 1797828911576383488 |
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| author | Maizan Syamimi Meor Yahaya Jason Teo Jason Teo |
| author_facet | Maizan Syamimi Meor Yahaya Jason Teo Jason Teo |
| author_sort | Maizan Syamimi Meor Yahaya |
| collection | DOAJ |
| description | The fields of medicine and neuroscience often face challenges in obtaining a sufficient amount of diverse data for training machine learning models. Data augmentation can alleviate this issue by artificially synthesizing new data from existing data. Generative adversarial networks (GANs) provide a promising approach for data augmentation in the context of images and biomarkers. GANs can synthesize high-quality, diverse, and realistic data that can supplement real data in the training process. This study provides an overview of the use of GANs for data augmentation in medicine and neuroscience. The strengths and weaknesses of various GAN models, including deep convolutional GANs (DCGANs) and Wasserstein GANs (WGANs), are discussed. This study also explores the challenges and ways to address them when using GANs for data augmentation in the field of medicine and neuroscience. Future works on this topic are also discussed. |
| first_indexed | 2024-04-09T13:12:00Z |
| format | Article |
| id | doaj.art-0f9d62ddaea448b9b1c4eff7d135feb3 |
| institution | Directory Open Access Journal |
| issn | 2297-4687 |
| language | English |
| last_indexed | 2024-04-09T13:12:00Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj.art-0f9d62ddaea448b9b1c4eff7d135feb32023-05-12T06:32:50ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872023-05-01910.3389/fams.2023.11627601162760Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscienceMaizan Syamimi Meor Yahaya0Jason Teo1Jason Teo2Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, MalaysiaAdvanced Machine Intelligence Research Group, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, MalaysiaEvolutionary Computing Laboratory, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, MalaysiaThe fields of medicine and neuroscience often face challenges in obtaining a sufficient amount of diverse data for training machine learning models. Data augmentation can alleviate this issue by artificially synthesizing new data from existing data. Generative adversarial networks (GANs) provide a promising approach for data augmentation in the context of images and biomarkers. GANs can synthesize high-quality, diverse, and realistic data that can supplement real data in the training process. This study provides an overview of the use of GANs for data augmentation in medicine and neuroscience. The strengths and weaknesses of various GAN models, including deep convolutional GANs (DCGANs) and Wasserstein GANs (WGANs), are discussed. This study also explores the challenges and ways to address them when using GANs for data augmentation in the field of medicine and neuroscience. Future works on this topic are also discussed.https://www.frontiersin.org/articles/10.3389/fams.2023.1162760/fulldata augmentationgenerative adversarial networksmedical imagesbiosignalsdisorder classificationdisease prediction |
| spellingShingle | Maizan Syamimi Meor Yahaya Jason Teo Jason Teo Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience Frontiers in Applied Mathematics and Statistics data augmentation generative adversarial networks medical images biosignals disorder classification disease prediction |
| title | Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience |
| title_full | Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience |
| title_fullStr | Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience |
| title_full_unstemmed | Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience |
| title_short | Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience |
| title_sort | data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience |
| topic | data augmentation generative adversarial networks medical images biosignals disorder classification disease prediction |
| url | https://www.frontiersin.org/articles/10.3389/fams.2023.1162760/full |
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