Improving COVID-19 CXR Detection with Synthetic Data Augmentation
Autor: | Schaudt, Daniel, Kloth, Christopher, Spaete, Christian, Hinteregger, Andreas, Beer, Meinrad, von Schwerin, Reinhold |
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Rok vydání: | 2021 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | Since the beginning of the COVID-19 pandemic, researchers have developed deep learning models to classify COVID-19 induced pneumonia. As with many medical imaging tasks, the quality and quantity of the available data is often limited. In this work we train a deep learning model on publicly available COVID-19 image data and evaluate the model on local hospital chest X-ray data. The data has been reviewed and labeled by two radiologists to ensure a high quality estimation of the generalization capabilities of the model. Furthermore, we are using a Generative Adversarial Network to generate synthetic X-ray images based on this data. Our results show that using those synthetic images for data augmentation can improve the model's performance significantly. This can be a promising approach for many sparse data domains. Comment: This paper has been accepted at the Upper-Rhine Artificial Intelligence Symposium 2021 arXiv:2112.05657 |
Databáze: | arXiv |
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