A self-learning teacher-student framework for gastrointestinal image classification

We present a semi-supervised teacher-student framework to improve classification performance on gastrointestinal image data. As labeled data is scarce in medical settings, this framework is built specifically to take advantage of vast amounts of unlabeled data. It consists of three main steps: (1) train a teacher model with labeled data, (2) use the teacher model to infer pseudo labels with unlabeled data, and (3) train a new and larger student model with a combination of labeled images and inferred pseudo labels. These three steps are repeated several times by treating the student as a teacher to relabel the unlabeled data and consequently train a new student. We demonstrate that our framework can classify both vide capsule endoscopy (VCE) and standard endoscopy images. Our results indicate that our teacher-student framework can significantly increase the performance compared to traditional supervised- learning-based models, i.e., an overall increase in the F-1-score of 1..7% for the Kvasir-Capsule VCE dataset and 3.2% for the HyperKvasir colonoscopy dataset. We believe that our framework can use more of the data collected at hospitals without the need for expert labels, contributing to overall better models for medical multimedia systems for automatic disease detection.