An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition

Quan Li, Yan Li, Xiaoyi Chen, Ni Zhang

Abstract: Accurate recognition of anatomy sites is important for evaluating the quality of esophagogastroduodenoscopy (EGD) examinations. However, because some anatomy sites have similar appearances and anatomical landmarks are lacking, gastric-anatomy image annotations are less than accurate. The annotations by doctors with various experience levels vary widely. Deep learning–based systems trained on these noisy annotations have poor recognition performance. In this work, we propose a novel data refinement approach to alleviate the problem of noisy annotations and improve the upper gastrointestinal anatomy recognition performance. In essence, we introduce a new uncertainty inference module for deep convolutional neural networks (CNNs) and leverage Bayesian uncertainty estimates to select possibly noisy data. In addition, we employ an ensemble of semi-supervised learning to rectify noisy labels and produce refined training data. We validate the proposed approach via controlled experiments on CIFAR-10, in which the noise rate is adjusted and noisy data are made known. It shows much improvement on classification accuracy using the refined dataset, and outperforms state-of-the-art robust training methods, e.g., MentorNet and Co-teaching. An evaluation of the upper gastrointestinal anatomy recognition task proves that our proposed method effectively improves the recognition accuracy for real, noisy clinical data. The proposed data refinement approach reduces the human effort needed to filter out and manually rectify noisy annotations. It can also be applied to wider scenarios where accurate expert labeling is expensive.