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Proceedings Paper

Chest x-ray generation and data augmentation for cardiovascular abnormality classification
Author(s): Ali Madani; Mehdi Moradi; Alexandros Karargyris; Tanveer Syeda-Mahmood
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Paper 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.

Paper Details

Date Published: 2 March 2018
PDF: 6 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741M (2 March 2018); doi: 10.1117/12.2293971
Show Author Affiliations
Ali Madani, IBM Research - Almaden (United States)
Mehdi Moradi, IBM Research - Almaden (United States)
Alexandros Karargyris, IBM Research - Almaden (United States)
Tanveer Syeda-Mahmood, IBM Research - Almaden (United States)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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