Stochastic Gastric Image Augmentation for Cancer Detection from X-ray Images

X-ray examinations arc a common choice in mass screenings for gastric cancer. Compared to end oscopy and other common modalities, X-ray examinations have the significant. advantage that they can be performed not only by radiologists but also by radiology technicians. However, the diagnosis of gastric X-ray images is very difficult and it has been reported that the diagnostic accuracy of these images is only 85.5%. In this study, we propose a practical diagnosis support system for gastric X-ray images. An important component of our system is the proposed on-line data augmentation strategy named stochastic gastric image augmentation (sGAIA), which stochastically generates various enhanced images of gastric folds in X-ray images. The proposed sGAIA improves the detection performance of the malignant region by 6.9% in F1-score and our system demonstrates promising screening performance for gastric cancer (recall of 92.3% with a precision of 32.4%) from X-ray images in a clinical setting based on Faster R-CNN with ResNet101 networks.