Hyun-bin Kim, Jun-Chul Chun, Journal of Internet Computing and Services, Vol. 24, No. 1, pp. 49-59, Feb. 2023
10.7472/jksii.2023.24.1.49, Full Text:  HTML
Keywords: Anomaly Detection, Chest Radiograph, computer vision, Semi-supervised learning, Image Feature Modeling

Abstract

Recently, attention to the pandemic situation represented by COVID-19 emerged problems caused by unexpected shortage of medical personnel. In this paper, we present a method for diagnosing the presence or absence of lesional sign on PA chest X-ray images as computer vision solution to support diagnosis tasks. Method for visual anomaly detection based on feature modeling can be also applied to X-ray images. With extracting feature vectors from PA chest X-ray images and divide to patch unit, region-specific abnormality can be detected. As preliminary experiment, we created simulation data set containing multiple objects and present results of the comparative experiments in this paper. We present method to improve both efficiency and performance of the process through hard masking of patch features to aligned images. By summing up regional specificity and global anomaly detection results, it shows improved performance by 0.069 AUROC compared to previous studies. By aggregating region-specific and global anomaly detection results, it shows improved performance by 0.069 AUROC compared to our last study.

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