Self-Supervised Learning for Gastritis Detection with Gastric X-ray Images

Purpose: It is time-consuming and expensive for doctors to annotate gastric X-ray images for gastritis detection manually. This paper proposes a self-supervised learning method to solve this problem. This study aims to verify the effectiveness of the proposed self-supervised learning method in gastritis detection using a few annotated gastric X-ray images. Methods: In this paper, we propose a novel method that can perform explicit self-supervised learning and learn discriminative representations from gastric X-ray images. Models trained with the proposed method were fine-tuned on datasets with a few annotated gastric X-ray images. Five self-supervised learning methods, i.e., SimSiam, BYOL, PIRL-jigsaw, PIRL-rotation, and SimCLR, were compared with the proposed method. Furthermore, three previous methods, one pretrained on ImageNet, one trained from scratch, and one semi-supervised learning method, were compared with the proposed method. Results: The proposed method$'$s harmonic mean score of sensitivity and specificity after fine-tuning with the annotated data of 10, 20, 30, and 40 patients were 0.875, 0.911, 0.915, and 0.931, respectively. The proposed method outperformed all comparative methods, including the five self-supervised learning and three previous methods. Experimental results showed the effectiveness of the proposed method in gastritis detection with a few annotated gastric X-ray images. Conclusions: The proposed self-supervised learning method shows potential clinical use in gastritis detection using a few annotated gastric X-ray images.