This research addresses the imbalance in medical imaging datasets, specifically in chest radiography, by leveraging generative adversarial networks (gans) for data augmentation. These datasets often suffer from severe. We compare performance to traditional.
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In this work, we propose a new gan architecture for augmentation of.
Nevertheless, data augmentation techniques for training gans are underexplored compared to cnns.
We compare performance to traditional. This study introduces a progressive.
