Chest x-ray generation and data augmentation for cardiovascular abnormality classification

Ali Madani; Mehdi Moradi; Alexandros Karargyris; Tanveer Syeda-Mahmood

doi:10.1117/12.2293971

Abstract

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

Citation Download Citation

Ali Madani, Ali Madani, Mehdi Moradi, Mehdi Moradi, Alexandros Karargyris, Alexandros Karargyris, Tanveer Syeda-Mahmood, 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); doi: 10.1117/12.2293971; https://doi.org/10.1117/12.2293971

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