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Generative adversarial network

Dr Yusra Sheikh and Dr Matt Adams et al.

Generative adversarial networks (GANs) are an elegant deep learning approach to generating fake data that is indistinguishable from real data. Two neural networks are paired off against one another (adversaries). The first network generates fake data to reproduce real data. The second, discriminative network, is tasked with trying to decide which is real and which is fake data. The process is repeated over and over, requiring both the generation of fake data and the detection of fake data to continuously improve. The importance of being able to produce realistic fake data, synthetic data, allows for the generative network to learn what features are important to pass as real data.

Generative adversarial networks in radiology

A recent publication demonstrated the use of GANs in the detection of congestive cardiac failure on chest radiographs with an overview provided in the AI's Black Box: Cracking Open The Chest X-ray Radiopaedia blog post 3. GANs are also being used for several image quality improvement algorithms eg. denoising algorithms 4.

Artificial intelligence

Article information

rID: 69034
Synonyms or Alternate Spellings:
  • Generative adversarial network (GAN)
  • Generative adversarial networks (GANs)

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