TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
X-ray Image Segmentation using Multi-task Learning |
| 영문제목(English Title) |
X-ray Image Segmentation using Multi-task Learning |
| 저자(Author) |
Sejin Park
Woojin Jeong
Young Shik Moon
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| 원문수록처(Citation) |
VOL 14 NO. 03 PP. 1104 ~ 1120 (2020. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
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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| 키워드(Keyword) |
image segmentation
convolutional neural network
lung nodule segmentation
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| 파일첨부 |
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