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GAN-based synthetic brain PET image generation.


ABSTRACT: In recent days, deep learning technologies have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated dataset. Obtaining such dataset for medical image analysis is very challenging. Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. We propose a novel approach to generate synthetic medical images using generative adversarial networks (GANs). Our proposed model can create brain PET images for three different stages of Alzheimer's disease-normal control (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD).

SUBMITTER: Islam J 

PROVIDER: S-EPMC7105582 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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GAN-based synthetic brain PET image generation.

Islam Jyoti J   Zhang Yanqing Y  

Brain informatics 20200330 1


In recent days, deep learning technologies have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated dataset. Obtaining such dataset for medical image analysis is very challenging. Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. We propose a novel approach to generate synthetic medical images using generative adversarial networks (GANs). Our proposed mod  ...[more]

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