Vision Transformers-Based Transfer Learning for Breast Mass Classification From Multiple Diagnostic Modalities
DC Field | Value | Language |
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dc.contributor.author | Ayana, Gelan | - |
dc.contributor.author | Choe, Se-woon | - |
dc.date.accessioned | 2024-05-02T13:00:23Z | - |
dc.date.available | 2024-05-02T13:00:23Z | - |
dc.date.issued | 2024-07 | - |
dc.identifier.issn | 1975-0102 | - |
dc.identifier.issn | 2093-7423 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28606 | - |
dc.description.abstract | Breast mass evaluation is crucial for early breast cancer diagnosis via imaging. While Convolutional Neural Network (CNN)-based deep learning (DL) has enhanced this process, it suffers from computational complexity and limited spatial encoding. Vision Transformer (ViT)-based DL, more adept at encoding spatial information, presents a promising alternative. This study introduces a ViT-based transfer learning (TL) method for breast mass classification. Three ViT-based TL architectures pretrained on ImageNet were proposed and evaluated using ultrasound and mammogram datasets. Comparative analysis against ViT trained from scratch and CNN-based TL was conducted. Results showed the ViT-based TL method achieving the highest area under curve (AUC) of 1 +/- 0 for both datasets, outperforming ViT from scratch and yielding similar or better performance compared to CNN-based TL. Despite its computational cost, ViT-based TL demonstrates superior classification capabilities for breast mass images. This research provides a foundational framework for future studies exploring ViT-based TL in breast cancer diagnosis. | - |
dc.format.extent | 20 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER SINGAPORE PTE LTD | - |
dc.title | Vision Transformers-Based Transfer Learning for Breast Mass Classification From Multiple Diagnostic Modalities | - |
dc.type | Article | - |
dc.publisher.location | 싱가폴 | - |
dc.identifier.doi | 10.1007/s42835-024-01904-w | - |
dc.identifier.scopusid | 2-s2.0-85189321980 | - |
dc.identifier.wosid | 001197348200001 | - |
dc.identifier.bibliographicCitation | JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, v.19, no.5, pp 3391 - 3410 | - |
dc.citation.title | JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY | - |
dc.citation.volume | 19 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 3391 | - |
dc.citation.endPage | 3410 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | CANCER | - |
dc.subject.keywordPlus | MAMMOGRAMS | - |
dc.subject.keywordAuthor | Vision Transformer | - |
dc.subject.keywordAuthor | Transfer Learning | - |
dc.subject.keywordAuthor | Breast Mass | - |
dc.subject.keywordAuthor | Ultrasound | - |
dc.subject.keywordAuthor | Mammography | - |
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