A voting-based ensemble model for improved automated classification of colorectal polyps including sessile serrated lesions

Aims Since sessile serrated lesions (SSL) were introduced, multiple AI studies have tried to classify them alongside hyperplastic (HYP) and adenomatous (ADN) polyps using machine learning, with varying results. A major hurdle for the classification of SSL is the low prevalence and difficult endoscopic recognition. Furthermore, the classification based on histology can also be subjective, as demonstrated by the inter-rater variability amongst pathologists. This study aims to improve the baseline training method for classification by training multiple models on subtasks before taking an ensemble vote for final classification.