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.
翻译:在本文中,我们提出并分析一个系统,可以自动检测、本地化和分类结肠镜录象中的聚苯胺聚苯醚,用聚苯胺检测仪检测框架是一个微小的异常分类问题,因为培训组与由普通图像组成的大多数框架和由聚苯醚组成的少数框架高度失衡。科洛诺复印视频可能包含模糊的图像和显示粪便和水喷喷雾的框架,以清理结肠 -- -- 这种框架可以被错误地检测为异常,因此我们在聚苯乙烯检测发生之前就应用了一个分类器拒绝这两类框架。接下来,如果有一个包含聚苯乙烯的框架,我们的方法本地化(在聚苯乙烯周围有一个捆绑框)并将其分为五个不同类别。此外,我们研究一种方法,用不确定性估计和分类校准来提高分类结果的可靠性和可解释性。分类不确定性和校准不仅有助于通过拒绝低自信和高不确定性的结果来提高分类的准确性,而且可由医生用来决定如何确定聚苯乙烯的分类。所有拟议的检测、本地化和分类方法都使用相关的大数据基比较方法。