Study on Optimal Generative Network for Synthesizing Brain Tumor-Segmented MR Images
DC Field | Value | Language |
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dc.contributor.author | Lee, Hyunhee | - |
dc.contributor.author | Jo, Jaechoon | - |
dc.contributor.author | Lim, Heuiseok | - |
dc.date.accessioned | 2021-08-30T23:37:38Z | - |
dc.date.available | 2021-08-30T23:37:38Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020-05-20 | - |
dc.identifier.issn | 1024-123X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/55662 | - |
dc.description.abstract | Due to institutional and privacy issues, medical imaging researches are confronted with serious data scarcity. Image synthesis using generative adversarial networks provides a generic solution to the lack of medical imaging data. We synthesize high-quality brain tumor-segmented MR images, which consists of two tasks: synthesis and segmentation. We performed experiments with two different generative networks, the first using the ResNet model, which has significant advantages of style transfer, and the second, the U-Net model, one of the most powerful models for segmentation. We compare the performance of each model and propose a more robust model for synthesizing brain tumor-segmented MR images. Although ResNet produced better-quality images than did U-Net for the same samples, it used a great deal of memory and took much longer to train. U-Net, meanwhile, segmented the brain tumors more accurately than did ResNet. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | HINDAWI LTD | - |
dc.title | Study on Optimal Generative Network for Synthesizing Brain Tumor-Segmented MR Images | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lim, Heuiseok | - |
dc.identifier.doi | 10.1155/2020/8273173 | - |
dc.identifier.scopusid | 2-s2.0-85085992741 | - |
dc.identifier.wosid | 000539211700010 | - |
dc.identifier.bibliographicCitation | MATHEMATICAL PROBLEMS IN ENGINEERING, v.2020 | - |
dc.relation.isPartOf | MATHEMATICAL PROBLEMS IN ENGINEERING | - |
dc.citation.title | MATHEMATICAL PROBLEMS IN ENGINEERING | - |
dc.citation.volume | 2020 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
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