• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

X-ray Image Segmentation using Multi-task Learning

Vol. 14, No. 3, March 31, 2020
10.3837/tiis.2020.03.011, Download Paper (Free):


The chest X-rays are a common way to diagnose lung cancer or pneumonia. In particular, the finding of a lung nodule is the most important problem in the early detection of lung cancer. Recently, a lot of automatic diagnosis algorithms have been studied to find the lung nodules missed by doctors. The algorithms are typically based on segmentation network like U-Net. However, the occurrence of false positives that similar to lung nodules present outside the lungs can severely degrade performance. In this study, we propose a multi-task learning method that simultaneously learns the lung region and nodule-labeled data based on the prior knowledge that lung nodules exist only in the lung. The proposed method significantly reduces false positives outside the lung and improves the recognition rate of lung nodules to 83.8 F1 score compared to 66.6 F1 score of single task learning with U-net model. The experimental results on the JSRT public dataset demonstrate the effectiveness of the proposed method compared with other baseline methods.


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Cite this article

[IEEE Style]
S. Park, W. Jeong and Y. S. Moon, "X-ray Image Segmentation using Multi-task Learning," KSII Transactions on Internet and Information Systems, vol. 14, no. 3, pp. 1104-1120, 2020. DOI: 10.3837/tiis.2020.03.011.

[ACM Style]
Sejin Park, Woojin Jeong, and Young Shik Moon. 2020. X-ray Image Segmentation using Multi-task Learning. KSII Transactions on Internet and Information Systems, 14, 3, (2020), 1104-1120. DOI: 10.3837/tiis.2020.03.011.

[BibTeX Style]
@article{tiis:23390, title="X-ray Image Segmentation using Multi-task Learning", author="Sejin Park and Woojin Jeong and Young Shik Moon and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.03.011}, volume={14}, number={3}, year="2020", month={March}, pages={1104-1120}}