Contrastive Self-Supervised Learning on Crohn’s Disease Detection

Abstract

Crohn’s disease is a type of inflammatory bowel illness that is typically identified v ia computer-aided diagnosis (CAD), which employs images from wireless capsule endoscopy (WCE). While deep learning has recently made significant advancements in Crohn’s disease detection, its performance is still constrained by limited labeled data. We suggest using contrastive self-supervised learning methods to address these difficulties which was barely used in detection of Crohn’s disease. Besides, we discovered that, unlike supervised learning, it is difficult to monitor contrastive self-supervised pretraining process in real time. So we propose a method for evaluating the model during contrastive pretraining (EDCP) based on the Euclidean distance of the sample representation, so that the model can be monitored during pretraining. Our comprehensive experiment results show that with contrastive self-supervised learning, better results in Crohn’s disease detection can be obtained. EDCP has also been shown to reflect the model’s training progress. Furthermore, we discovered some intriguing issues with using contrastive self-supervised learning for small dataset tasks in our experiments that merit further investigation.