유방 영상에서 딥러닝 기반의 유방 종괴 자동 분할 연구
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 권소윤 | - |
dc.contributor.author | 김영재 | - |
dc.contributor.author | 김광기 | - |
dc.date.available | 2020-02-27T13:41:09Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2018-12 | - |
dc.identifier.issn | 1229-7771 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4481 | - |
dc.description.abstract | Breast cancer is one of the most common cancers in women worldwide. In Korea, breast cancer is most common cancer in women followed by thyroid cancer. The purpose of this study is to evaluate the possibility of using deep - run model for segmentation of breast masses and to identify the best deep-run model for breast mass segmentation. In this study, data of patients with breast masses were collected at Asan Medical Center. We used 596 images of mammography and 596 images of gold standard. In the area of interest of the medical image, it was cut into a rectangular shape with a margin of about 10% up and down, and then converted into an 8-bit image by adjusting the window width and level. Also, the size of the image was resampled to 150x150. In Deconvolution net, the average accuracy is 91.78%. In U-net, the average accuracy is 90.09%. Deconvolution net showed slightly better performance than U-net in this study, so it is expected that deconvolution net will be better for breast mass segmentation. However, because of few cases, there are a few images that are not accurately segmented. Therefore, more research is needed with various training data. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 한국멀티미디어학회 | - |
dc.relation.isPartOf | 멀티미디어학회논문지 | - |
dc.title | 유방 영상에서 딥러닝 기반의 유방 종괴 자동 분할 연구 | - |
dc.title.alternative | An Automatic Breast Mass Segmentation based on Deep Learning on Mammogram | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 2 | - |
dc.identifier.doi | 10.9717/kmms.2018.21.12.1363 | - |
dc.identifier.bibliographicCitation | 멀티미디어학회논문지, v.21, no.12, pp.1363 - 1369 | - |
dc.identifier.kciid | ART002424835 | - |
dc.citation.endPage | 1369 | - |
dc.citation.startPage | 1363 | - |
dc.citation.title | 멀티미디어학회논문지 | - |
dc.citation.volume | 21 | - |
dc.citation.number | 12 | - |
dc.contributor.affiliatedAuthor | 권소윤 | - |
dc.contributor.affiliatedAuthor | 김영재 | - |
dc.contributor.affiliatedAuthor | 김광기 | - |
dc.subject.keywordAuthor | Artificial Intelligence | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Breast Mass | - |
dc.subject.keywordAuthor | Mammograpy | - |
dc.subject.keywordAuthor | Segmentation | - |
dc.description.journalRegisteredClass | kci | - |
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