Feb. 6, 2024, 5:53 a.m. | Oleksandr Fedoruk Konrad Klimaszewski Aleksander Ogonowski Micha{\l} Kruk

cs.CV updates on arXiv.org arxiv.org

The availability of training data is one of the main limitations in deep learning applications for medical imaging. Data augmentation is a popular approach to overcome this problem. A new approach is a Machine Learning based augmentation, in particular usage of Generative Adversarial Networks (GAN). In this case, GANs generate images similar to the original dataset so that the overall training data amount is bigger, which leads to better performance of trained networks. A GAN model consists of two networks, …

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