Helicobacter Pylori Infection Diagnosis from Endoscopic Images via Multi-Class Token based Multiple Instance Learning

To diagnose Helicobacter pylori (H. pylori) infection from endoscopic images, conventional methods require a time-consuming labeling process to annotate individual endoscopic images based on pathological findings. In this paper, we aim to diagnose H. pylori infection for each patient from a group of endoscopic images captured during endoscopy, where only patient-level labels are available for each patient. To achieve the goal, we propose a multi-class token-based multiple instance learning method which consists of the feature extractor, the multi-class token selector module, and the aggregator. By using learnable positive class tokens and negative class tokens with transformer encoders, the multi-class token selector module selects the proper class tokens to improve the H. pylori infection prediction performance of the aggregator. Compared with supervised methods and state-of-the-art multiple instance learning methods, the proposed method achieves the best results.