Deep reconstruction-recoding network for unsupervised domain adaptation and multi-center generalization in colonoscopy polyp detection. (February 2022)

• Record Type: Journal Article
• Authors: Xu, Jianwei; Zhang, Qingwei; Yu, Yizhou; Zhao, Ran; Bian, Xianzhang; Liu, Xiaoqing; Wang, Jun; Ge, Zhizheng; Qian, Dahong
• Is Part Of: Computer methods and programs in biomedicine. Volume 214(2022)
• Publication Date: 2022-02
• Subjects: Polyp detection -- Domain adaptation -- Multi center generalization -- Adversarial learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106576
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We discuss the domain shift and domain adaptation problems in polyp detection datasets from different hospitals or endoscope models. To the best of our knowledge, this is the first paper to discuss the domain distribution differences between polyp detection datasets. We propose a novel end-to-end network, which has two pipelines, to simultaneously learn the feature distribution of the source and target domains and reduce the distribution difference in the feature space of the two domains. The proposed method requires only a small amount of unlabeled data from the target domain to improve the performance of the polyp detection model on the target domain. The DRRN is very simple, easy to implement, and can be easily transferred to other tasks. It does not require additional adversarial networks, so the network is easy to train, converges quickly, and has fewer parameters. Abstract: Background and objective: Currently, the best performing methods in colonoscopy polyp detection are primarily based on deep neural networks (DNNs), which are usually trained on large amounts of labeled data. However, different hospitals use different endoscope models and set different imaging parameters, which causes the collected endoscopic images and videos to vary greatly in style. There may be variations in the color space, brightness, contrast, and resolution, and there are also differences between white light endoscopy (WLE) and narrow band image endoscopy (NBIE). We call these variations the domain shift. The DNN performance may decrease when the training data and the testing data come from different hospitals or different endoscope models. Additionally, it is quite difficult to collect enough new labeled data and retrain a new DNN model before deploying that DNN to a new hospital or endoscope model. Methods: To solve this problem, we propose a domain adaptation model called Deep Reconstruction-Recoding Network (DRRN), which jointly learns a shared encoding representation for two tasks: i) a supervised object detection network for labeled source data, and ii) an unsupervised reconstruction-recoding network for unlabeled target data. Through the DRRN, the object detection network's encoder not only learns the features from the labeled source domain, but also encodes useful information from the unlabeled target domain. Therefore, the distribution difference of the two domains' feature spaces can be reduced. Results: We evaluate the performance of the DRRN on a series of cross-domain datasets. Compared with training the polyp detection network using only source data, the performance of the DRRN on the target domain is improved. Through feature statistics and visualization, it is demonstrated that the DRRN can learn the common distribution and feature invariance of the two domains. The distribution difference between the feature spaces of the two domains can be reduced. Conclusion: The DRRN can improve cross-domain polyp detection. With the DRRN, the generalization performance of the DNN-based polyp detection model can be improved without additional labeled data. This improvement allows the polyp detection model to be easily transferred to datasets from different hospitals or different endoscope models.