Automatic Bleeding Risk Rating System of Gastric Varices

An automated bleeding risk rating system of gastric varices (GV) aims to predict the bleeding risk and severity of GV, in order to assist endoscopists in diagnosis and decrease the mortality rate of patients with liver cirrhosis and portal hypertension. However, since the lack of commonly accepted quantification standards, the risk rating highly relies on the endoscopists' experience and may vary a lot in different application scenarios. In this work, we aim to build an automatic GV bleeding risk rating method that can learn from experienced endoscopists and provide stable and accurate predictions. Due to the complexity of GV structures with large intra-class variation and small inter-class variations, we found that existing models perform poorly on this task and tend to lose focus on the important varices regions. To solve this issue, we constructively introduce the segmentation of GV into the classification framework and propose the region-constraint module and cross-region attention module for better feature localization and to learn the correlation of context information. We also collect a GV bleeding risks rating dataset (GVbleed) with 1678 gastroscopy images from 411 patients that are jointly annotated in three levels of risks by senior clinical endoscopists. The experiments on our collected dataset show that our method can improve the rating accuracy by nearly 5% compared to the baseline. Codes and dataset will be available at https://github.com/LuyueShi/gastric-varices.