Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
This addresses data scarcity in medical imaging for improved diagnostic accuracy, but it is incremental as it builds on existing GAN and data augmentation methods.
The paper tackled the problem of limited medical image data for liver lesion classification by using GAN-based synthetic data augmentation, resulting in improved sensitivity from 78.6% to 85.7% and specificity from 88.4% to 92.4%.
In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results significantly increased to 85.7% sensitivity and 92.4% specificity.