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Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy. |
| 개인저자 | Antropova, Natalia. |
| 단체저자명 | The University of Chicago. Medical Physics. |
| 발행사항 | [S.l.]: The University of Chicago., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 143 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-11B(E). Dissertation Abstract International |
| ISBN | 9780438083431 |
| 학위논문주기 | Thesis (Ph.D.)--The University of Chicago, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Adviser: Maryellen Giger. |
| 요약 | Breast cancer is found in one in eight women in the United States and is expected to be the most frequently diagnosed form of cancer among them in 2018. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a significant role in b |
| 요약 | Radiomcs has strong potential to lead clinicians towards more accurate and rapid image interpretation. Furthermore, it can serve as a "virtual digital biopsy", allowing for the discovery of relationships between radiomics and the pathology/genom |
| 요약 | The research presented the following results. First, the robustness analysis revealed radiomics features that are generalizable across datasets acquired with MRI scanners of two major manufacturers. Specifically, features that characterize lesio |
| 요약 | The medical significance of this research is that it has potential to improve DCE-MRI-based breast cancer management. The developed deep learning methods and their fusion with conventional radiomics can reduce human burden and allow for more rap |
| 일반주제명 | Medical imaging. Artificial intelligence. Oncology. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
