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Found 3 papers, 1 papers with code
Non-invasive Liver Fibrosis Screening on CT Images using Radiomics
The combination of hyperparameters and features that yielded the highest AUC was a logistic regression model with inputs features of maximum, energy, kurtosis, skewness, and small area high gray level emphasis extracted from non-contrast enhanced NC CT normalized using Gamma correction with $\gamma$ = 1. 5 (AUC, 0. 7833; 95% CI: 0. 7821, 0. 7845), (sensitivity, 0. 9091; 95% CI: 0. 9091, 0. 9091).
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray
Gathering labeled data is a cumbersome task and requires time and resources which could further strain health care systems and radiologists at the early stages of a pandemic such as COVID-19.
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images
Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively.
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