Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets , networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve CNN performance . Nevertheless, generative models have not been used for augmenting data to improve the training of another generative model . In this work, we propose a new GAN architecture for semi-supervised augmentation of chest X-rays for the detection of pneumonia  . We show that the proposed GAN can augment data for a specific class of images (pneumonia) using images from both classes (pneumonia and normal) in an image domain ( chest X-rays ). We demonstrate that using our proposed GAN-based data augmentation method significantly improves the performance of the state-of-the-art anomaly detection architecture, AnoGAN, in detecting pneumonia in chest X-rays, increasing AUC from 0.83 to 0.88 .
Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs . Nevertheless, data augmentation techniques for training GANs are under-explored compared to CNNs . In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models . We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images .
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,
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CNN performance
the performance of CNNs
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generative models have not been used for augmenting data to improve the training of another generative model
data augmentation techniques for training GANs are under-explored compared to CNNs
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semi-supervised
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the
semi-supervised
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and COVID-19 using generative models
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augment data for a specific class of images (pneumonia) using images from both classes (pneumonia and normal) in an image domain (
be used to effectively augment data and improve classification accuracy of disease in
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). We demonstrate that using our proposed
for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc) on two different X-ray datasets and show our
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data augmentation method significantly improves the performance of the state-of-the-art anomaly detection architecture, AnoGAN, in detecting pneumonia in chest X-rays, increasing AUC from 0.83 to 0.88
augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images