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3-D breast nodule detection on automated breast ultrasound using faster region-based convolutional neural networks and U-Net
DC Field | Value | Language |
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dc.contributor.author | 김은경 | - |
dc.contributor.author | 이시은 | - |
dc.contributor.author | 오강록 | - |
dc.date.accessioned | 2024-01-03T01:37:49Z | - |
dc.date.available | 2024-01-03T01:37:49Z | - |
dc.date.issued | 2023-12 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/197623 | - |
dc.description.abstract | Mammography is currently the most commonly used modality for breast cancer screening. However, its sensitivity is relatively low in women with dense breasts. Dense breast tissues show a relatively high rate of interval cancers and are at high risk for developing breast cancer. As a supplemental screening tool, ultrasonography is a widely adopted imaging modality to standard mammography, especially for dense breasts. Lately, automated breast ultrasound imaging has gained attention due to its advantages over hand-held ultrasound imaging. However, automated breast ultrasound imaging requires considerable time and effort for reading because of the lengthy data. Hence, developing a computer-aided nodule detection system for automated breast ultrasound is invaluable and impactful practically. This study proposes a three-dimensional breast nodule detection system based on a simple two-dimensional deep-learning model exploiting automated breast ultrasound. Additionally, we provide several postprocessing steps to reduce false positives. In our experiments using the in-house automated breast ultrasound datasets, a sensitivity of [Formula: see text] with 8.6 false positives is achieved on unseen test data at best. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Breast / diagnostic imaging | - |
dc.subject.MESH | Breast Density | - |
dc.subject.MESH | Breast Neoplasms* / diagnostic imaging | - |
dc.subject.MESH | Early Detection of Cancer / methods | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Mammography / methods | - |
dc.subject.MESH | Neural Networks, Computer | - |
dc.subject.MESH | Ultrasonography, Mammary / methods | - |
dc.title | 3-D breast nodule detection on automated breast ultrasound using faster region-based convolutional neural networks and U-Net | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Kangrok Oh | - |
dc.contributor.googleauthor | Si Eun Lee | - |
dc.contributor.googleauthor | Eun-Kyung Kim | - |
dc.identifier.doi | 10.1038/s41598-023-49794-8 | - |
dc.contributor.localId | A00801 | - |
dc.contributor.localId | A05611 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 38114666 | - |
dc.contributor.alternativeName | Kim, Eun Kyung | - |
dc.contributor.affiliatedAuthor | 김은경 | - |
dc.contributor.affiliatedAuthor | 이시은 | - |
dc.citation.volume | 13 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 22625 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.13(1) : 22625, 2023-12 | - |
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