Automatic Hepatocellular Carcinoma Diagnosis using Graph Convolutional Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Yushin | - |
dc.contributor.author | Kim, Jaehyeon | - |
dc.contributor.author | Lee, Sejong | - |
dc.contributor.author | Ahn, Seyoung | - |
dc.contributor.author | Kim, Jonghun | - |
dc.contributor.author | Park, Sooyoung | - |
dc.contributor.author | Cho, Sunghyun | - |
dc.date.accessioned | 2023-05-03T09:32:58Z | - |
dc.date.available | 2023-05-03T09:32:58Z | - |
dc.date.issued | 2022-02 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/112534 | - |
dc.description.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. | - |
dc.format.extent | 4 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE | - |
dc.title | Automatic Hepatocellular Carcinoma Diagnosis using Graph Convolutional Network | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ICEIC54506.2022.9748503 | - |
dc.identifier.scopusid | 2-s2.0-85128864591 | - |
dc.identifier.wosid | 000942023400067 | - |
dc.identifier.bibliographicCitation | 2022 International Conference on Electronics, Information, and Communication (ICEIC), pp 1 - 4 | - |
dc.citation.title | 2022 International Conference on Electronics, Information, and Communication (ICEIC) | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 4 | - |
dc.type.docType | Proceedings Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | sci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordAuthor | Computer-aided diagnosis | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Graph convolutional networks | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9748503 | - |
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