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2 March 2018 Chest x-ray generation and data augmentation for cardiovascular abnormality classification
Ali Madani, Mehdi Moradi, Alexandros Karargyris, Tanveer Syeda-Mahmood
Author Affiliations +
Medical imaging datasets are limited in size due to privacy issues and the high cost of obtaining annotations. Augmentation is a widely used practice in deep learning to enrich the data in data-limited scenarios and to avoid overfitting. However, standard augmentation methods that produce new examples of data by varying lighting, field of view, and spatial rigid transformations do not capture the biological variance of medical imaging data and could result in unrealistic images. Generative adversarial networks (GANs) provide an avenue to understand the underlying structure of image data which can then be utilized to generate new realistic samples. In this work, we investigate the use of GANs for producing chest X-ray images to augment a dataset. This dataset is then used to train a convolutional neural network to classify images for cardiovascular abnormalities. We compare our augmentation strategy with traditional data augmentation and show higher accuracy for normal vs abnormal classification in chest X-rays.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ali Madani, Mehdi Moradi, Alexandros Karargyris, and Tanveer Syeda-Mahmood "Chest x-ray generation and data augmentation for cardiovascular abnormality classification", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741M (2 March 2018);

Cited by 56 scholarly publications and 1 patent.
Chest imaging

Data modeling

Medical imaging

Convolutional neural networks

Image classification

Machine learning


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