Learning From Limited and Imbalanced Medical Images With Finer Synthetic Images From GANs

Abstract

Chest X-ray is a prevalent medical imaging modality for detecting lung diseases. The clinical analysis of X-ray images is usually conducted by radiologists, who represent valuable human resources. In practice, situations with insufficient radiologists to timely analyze large quantities of X-ray data are very common. Accordingly, developing an automated computer-aided lung disease classification system is beneficial to facilitate diagnoses. However, due to restrictions of costs and time, collecting large amounts of accurately labeled X-ray images to train a machine learning based diagnosis system is challenging. Another limitation is the class imbalances present in datasets. Facing these challenges, we investigate the effectiveness of using generative models, particularly generative adversarial networks (GANs), to synthesize new data to tackle the issue of data paucity and class imbalances. To this end, it should be noted that few existing works have studied the effect of generated image quality on the performance of different learning models, particularly in medical imaging. Therefore, the current paper represents one of the first comprehensive investigations into the impact of synthetic image generation on classifier performance, which is empirically elucidated by a comparative analysis of a simpler deep convolutional GAN to a more complex progressive GAN design. Another contribution of this paper is a multi-scale convolutional neural network (CNN) architecture, which can take advantage of image features at different scales for better learning from scratch. Altogether, to verify the robustness of using GANs to augment datasets, we compare various data augmentation approaches, when applied to different network architectures, including transfer learning, learning-from-scratch CNNs, state-of-the-art ResNet, EfficientNet, DenseNet, and the proposed multi-scale CNN. Specifically, testing on two publicly available datasets, the obtained results show that using finer images synthesized from GANs with the proposed multi-scale CNN achieved good classification performance, under a wide range of operating conditions.