Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease
This work addresses the problem of limited medical imaging data for clinicians and researchers, though it is incremental as it builds on existing GAN methods for a specific domain.
The authors tackled the scarcity of labeled ultrasound data for liver disease classification by proposing a novel GAN architecture to synthesize realistic liver ultrasound images, resulting in significantly improved performance for NAFLD classification when combined with real data.
With the success of deep learning-based methods applied in medical image analysis, convolutional neural networks (CNNs) have been investigated for classifying liver disease from ultrasound (US) data. However, the scarcity of available large-scale labeled US data has hindered the success of CNNs for classifying liver disease from US data. In this work, we propose a novel generative adversarial network (GAN) architecture for realistic diseased and healthy liver US image synthesis. We adopt the concept of stacking to synthesize realistic liver US data. Quantitative and qualitative evaluation is performed on 550 in-vivo B-mode liver US images collected from 55 subjects. We also show that the synthesized images, together with real in vivo data, can be used to significantly improve the performance of traditional CNN architectures for Nonalcoholic fatty liver disease (NAFLD) classification.