Few Trust Data Guided Annotation Refinement for Upper Gastrointestinal Anatomy Recognition

Abstract

The performance of anatomy site recognition is critical for computer-aided diagnosis systems such as the quality evaluation of endoscopic examinations and the automatic generating of electronic medical records. To achieve an accurate recognition model, it requires extensive training samples and precise annotations from human experts, especially for deep learning based methods. However, due to the similar appearance of gastrointestinal (GI) anatomy sites, it is hard to annotate accurately and is expensive to acquire such high quality dataset at a large scale. Therefore, to balance the cost-performance trade-offs, in this work we propose an effective annotation refinement approach which leverages a small amount of trust data that is accurately labelled by experts to further improve the training performance on a large amount of noisy label data. In particular, we adopt noise robust training on noisy dataset with additional constraints of adaptively assigned sample weights that are learned from trust data. Controlled experiments on synthetic datasets with generated noisy annotations validate the effectiveness of our proposed method. For practical use, we design a manual process to come up with a small amount of trust data with reliable annotations from a noisy upper GI dataset. Experimental evaluations validates that leveraging a small amount of trust data can effectively rectify incorrect labels and improve testing accuracy on noisy datasets. Our proposed annotation refinement approach provides a cost effective solution to acquire high quality annotations for medical image recognition tasks.