  In this paper, we propose and analyse a system that can automatically detect,
localise and classify polyps from colonoscopy videos. The detection of frames
with polyps is formulated as a few-shot anomaly classification problem, where
the training set is highly imbalanced with the large majority of frames
consisting of normal images and a small minority comprising frames with polyps.
Colonoscopy videos may contain blurry images and frames displaying feces and
water jet sprays to clean the colon -- such frames can mistakenly be detected
as anomalies, so we have implemented a classifier to reject these two types of
frames before polyp detection takes place. Next, given a frame containing a
polyp, our method localises (with a bounding box around the polyp) and
classifies it into five different classes. Furthermore, we study a method to
improve the reliability and interpretability of the classification result using
uncertainty estimation and classification calibration. Classification
uncertainty and calibration not only help improve classification accuracy by
rejecting low-confidence and high-uncertain results, but can be used by doctors
to decide how to decide on the classification of a polyp. All the proposed
detection, localisation and classification methods are tested using large data
sets and compared with relevant baseline approaches.
