Improving COVID-19 CXR Detection with Synthetic Data Augmentation

Autor:	Schaudt, Daniel, Kloth, Christopher, Spaete, Christian, Hinteregger, Andreas, Beer, Meinrad, von Schwerin, Reinhold
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
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
Externí odkaz:	http://arxiv.org/abs/2112.07529 Zobrazit plný text záznamu View this record from Arxiv