Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience

Maizan Syamimi Meor Yahaya and Jason Teo (2023) Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience. Applied Mathematics and Statistics. pp. 1-8.

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Abstract

The fields of medicine and neuroscience often face challenges in obtaining a su cient 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.

Item Type:	Article
Keyword:	data augmentation, generative adversarial networks, medical images, biosignals, disorder classification, disease prediction
Subjects:	Q Science > Q Science (General) > Q1-390 Science (General) > Q300-390 Cybernetics R Medicine > RC Internal medicine > RC31-1245 Internal medicine > RC71-78.7 Examination. Diagnosis Including radiography
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	ABDULLAH BIN SABUDIN -
Date Deposited:	12 Jun 2024 10:00
Last Modified:	12 Jun 2024 10:00
URI:	https://eprints.ums.edu.my/id/eprint/38820

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