자료유형 | 학위논문 |
---|---|
서명/저자사항 | Characterizing Pulmonary Nodules using Machine and Deep Learning Methods to Improve Lung Cancer Diagnosis. |
개인저자 | Shen, Shiwen. |
단체저자명 | University of California, Los Angeles. Bioengineering 0288. |
발행사항 | [S.l.]: University of California, Los Angeles., 2018. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
형태사항 | 137 p. |
기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
ISBN | 9780438030497 |
학위논문주기 | Thesis (Ph.D.)--University of California, Los Angeles, 2018. |
일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Advisers: Alex AT Bui |
요약 | Low-dose computed tomography (CT) screening has been widely used to detect and diagnose early stage lung cancer. Clinical trials have shown that low-dose CT reduced lung cancer mortality by 20% relative to plain chest radiography |
일반주제명 | Biomedical engineering. Computer science. |
언어 | 영어 |
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: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |