Automatic Hepatocellular Carcinoma Diagnosis using Graph Convolutional Network
- Title
- Automatic Hepatocellular Carcinoma Diagnosis using Graph Convolutional Network
- Author
- 조성현
- Keywords
- Bioengineering; Communication, Networking and Broadcast Technologies; Components, Circuits, Devices and Systems; Computing and Processing; Engineered Materials, Dielectrics and Plasmas; Fields, Waves and Electromagnetics; Photonics and Electrooptics; Training; Knowledge engineering; Degradation; Computational modeling; Data models; Computer aided diagnosis; Blood; Computer-aided diagnosis; Deep learning; Graph convolutional networks
- Issue Date
- 2022-02
- Publisher
- IEEE
- Citation
- 2022 International Conference on Electronics, Information, and Communication (ICEIC) Electronics, Information, and Communication (ICEIC), 2022 International Conference on. :1-4 Feb, 2022
- Abstract
- Blood tests are used to screen a risk group for
hepatocellular carcinoma. Various studies have utilized artificial
intelligence to diagnose hepatocellular carcinoma using blood
test records. However, most studies suffer from performance
degradation due to insufficient data. In this paper, we propose
a novel graph convolutional network-based computer-aided diagnosis model to address the data insufficiency problem. The
proposed method assists training by converting data into graphs
representing the relationships among the features. As a result,
our diagnosis model has improved 4% accuracy compared to
existing approaches with 89.3% accuracy.
- URI
- https://ieeexplore.ieee.org/document/9748503?arnumber=9748503&SID=EBSCO:edseeehttps://repository.hanyang.ac.kr/handle/20.500.11754/171056
- ISBN
- 978-1-6654-0934-6
- ISSN
- 2767-7699
- DOI
- 10.1109/ICEIC54506.2022.9748503
- Appears in Collections:
- ETC[S] > 연구정보
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