• Journal of Internet Computing and Services
    ISSN 2287 - 1136(Online) / ISSN 1598 - 0170 (Print)
    http://jics.or.kr/

Leision Detection in Chest X-ray Images based on Coreset of Patch Feature


Hyun-bin Kim, Jun-Chul Chun, Journal of Internet Computing and Services, Vol. 23, No. 3, pp. 35-45, Jun. 2022
10.7472/jksii.2022.23.3.35, Full Text:
Keywords: Anomaly Detection, X-Ray image, active learning, unsupervision, Deep Learning

Abstract

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[APA Style]
Hyun-bin Kim and Jun-Chul Chun (2022). Leision Detection in Chest X-ray Images based on Coreset of Patch Feature. Journal of Internet Computing and Services, 23(3), 35-45. DOI: 10.7472/jksii.2022.23.3.35.

[IEEE Style]
H. Kim and J. Chun, "Leision Detection in Chest X-ray Images based on Coreset of Patch Feature," Journal of Internet Computing and Services, vol. 23, no. 3, pp. 35-45, 2022. DOI: 10.7472/jksii.2022.23.3.35.

[ACM Style]
Hyun-bin Kim and Jun-Chul Chun. 2022. Leision Detection in Chest X-ray Images based on Coreset of Patch Feature. Journal of Internet Computing and Services, 23, 3, (2022), 35-45. DOI: 10.7472/jksii.2022.23.3.35.