Quality controlling in capsule gastroduodenoscopy with less annotation via self-supervised learning

Background It is possible to control the quality of capsule endoscopic images using artificial intelligence (AI), but it requires a great deal of time for labeling. Methods SimCLR (a simple framework for contrastive learning of visual representations), is capable of acquiring the inherent image representation with minimal annotation, but the feasibility is not studied. 62850 images were collected to train models. In internal cross-validation (more training data and less testing data) and reversed cross-validation (less training data and more testing data). Random forest and Xgboost (eXtreme Gradient Boosting) were used to finish the quality controlling after SimCLR extracting the features from images. Results SimCLR reported that the mean AUROC (Area Under the Receiver Operating Characteristic) curve exceeded 0.98 and 0.97. Moreover, Xgboost surpassed supervised CNN (Convolutional Neural Network). Extra 18636 pictures were gathered and the AUROC of SimCLR surpassed 0.93 (95% CI 0.9271–0.9548), which is close to supervised CNN (Convolutional Neural Network) (0.9645) in cross validation. Moreover, the AUROC of SimCLR surpass 0.96, which is better than supervised CNN (0.8374) in reversed cross validation. Conclusions Through SimCLR, the capsule endoscopic image quality control task can be completed with a performance similar to or better than that of supervised learning with fewer annotations.