GAN-Based Synthetic Gastrointestinal Image Generation

As with several medical image analysis tasks based on deep learning, gastrointestinal image analysis is plagued with data scarcity, privacy concerns and insufficient number of pathology samples. This paper frames the task of generating new and plausible samples of oesophageal cancer images as an image-to-image translation task. We evaluate two adversarial trained fully convolutional network architectures to generate synthetic diseased gastrointestinal images from segmentation maps prepared by expert clinicians. The synthetic images are evaluated both qualitatively and quantitively and the experimental results indicate that they are comparable to the original images. This potentially addresses the challenge of data scarcity and the need for heavy data augmentation in automated gastrointestinal image analysis.