A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0

• Main Authors: Huang, Junjun, Saw, Shier Nee, He, Tianran, Feng, Wei, Loo, Chu Kiong
• Format: Article
• Published: ELSEVIER 2024
• Subjects: QA75 Electronic computers. Computer science; TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.um.edu.my/44155/; https://doi.org/10.1016/j.jii.2023.100467

The emergence of Healthcare 4.0 brings convenience to the diagnosis of gastric polyps patients. The computer aided gastric polyp detection model can automatically locate the position of gastric polyps in gastroscopic images, which helps endoscopists to detect gastric polyps in time and reduce the rate of missed diagnosis. The deep learning model has achieved remarkable success in the field of gastroscopic images, however, it still has the following problems to be solved. Firstly, the model based on the convolutional neural network only analyzes the underlying pixels of the gastroscopic image to locate the polyp, which does not take into account the spatial and positional information contained in the anatomical structure of the gastroscopic image. Secondly, although the number of gastroscopic images is huge, the number of manually annotated gastric polyp images is very small, which makes the deep learning model prone to overfitting. Therefore, in this work, we propose a masked graph neural network model (MGNN) for real-time detecting the location of polyps in gastroscopic images in the Healthcare 4.0. The MGNN model novelly utilizes the graph structure and graph convolution operations to extract spatial location information and semantic information of the gastroscopic images. The information from masked self-training is additionally considered in the prediction value stage to compensate for the deficiency in the number of manually labeled gastric polyp images. In this way, the MGNN model can automatically learn the essential features of gastroscopic images without labeling data. The effectiveness of the MGNN model has been verified on real gastroscope images.