Polyp Detection in Colonoscopy Images using Deep Learning and Bootstrap Aggregation

Computer-aided polyp detection is playing an increasingly more important role in the colonoscopy procedure. Although many methods have been proposed to tackle the polyp detection problem, their out-of-distribution test results, which is an important indicator of their clinical readiness, are not demonstrated. In this study, we propose an ensemble-based polyp detection pipeline for detecting polyps in colonoscopy images. We train various models from EfficientDet family on both the EndoCV2021 and the Kvasir-SEG datasets, and evaluate their performances on these datasets both in- and out-of-distribution manner. The proposed architecture works in near real-time due to the efficiency of the EfficientDet architectures even when used in an ensemble setting