SynSigGAN: Generative Adversarial Networks for Synthetic Biomedical Signal Generation
Automating medical diagnosis and training medical students with real-life situations requires the accumulation of large dataset variants covering all aspects of a patient’s condition. For preventing the misuse of patient’s private information, datasets are not always publicly available. There is a n...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Biology |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-7737/9/12/441 |