Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks
This work addresses data imbalance issues in medical imaging for clinicians, but it is incremental as it applies an existing GAN method to a specific domain.
The paper tackled the problem of imbalanced medical datasets for chest X-ray pathology classification by using a generative adversarial network (GAN) to create artificial images, which improved classification performance when combined with real data.
Medical datasets are often highly imbalanced with over-representation of common medical problems and a paucity of data from rare conditions. We propose simulation of pathology in images to overcome the above limitations. Using chest X-rays as a model medical image, we implement a generative adversarial network (GAN) to create artificial images based upon a modest sized labeled dataset. We employ a combination of real and artificial images to train a deep convolutional neural network (DCNN) to detect pathology across five classes of chest X-rays. Furthermore, we demonstrate that augmenting the original imbalanced dataset with GAN generated images improves performance of chest pathology classification using the proposed DCNN in comparison to the same DCNN trained with the original dataset alone. This improved performance is largely attributed to balancing of the dataset using GAN generated images, where image classes that are lacking in example images are preferentially augmented.