Generative adversarial network
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At the time the article was created Matt Adams had no recorded disclosures.View Matt Adams's current disclosures
At the time the article was last revised Dimitrios Toumpanakis had no recorded disclosures.View Dimitrios Toumpanakis's current disclosures
Generative adversarial networks (GANs) are an elegant deep learning approach to generating artificial data that is indistinguishable from real data. Two neural networks are paired off against one another (adversaries). The first network generates artificial data to reproduce real data. The second, discriminative network, is tasked with trying to decide which is real and which is artificial data. The process is repeated over and over, requiring both the generation of artificial data and the detection of artificial data to continuously improve. The importance of being able to produce realistic artificial 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.
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- 2. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio (2014). Generative Adversarial Network. Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014). pp. 2672–2680.
- 3. Seah JCY, Tang JSN, Kitchen A, Gaillard F, Dixon AF. Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning. (2019) Radiology. 290 (2): 514-522. doi:10.1148/radiol.2018180887 - Pubmed
- 4. Shan H, Zhang Y, Yang Q, Kruger U, Kalra MK, Sun L, Cong W, Wang G. 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network. (2018) IEEE transactions on medical imaging. 37 (6): 1522-1534. doi:10.1109/TMI.2018.2832217 - Pubmed