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Characterizing Pulmonary Nodules using Machine and Deep Learning Methods to Improve Lung Cancer Diagnosis

机译：使用机器学习和深度学习方法表征肺结节以改善肺癌诊断

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作者
Shen, Shiwen.;
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作者单位

University of California, Los Angeles.;

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授予单位 University of California, Los Angeles.;
学科 Biomedical engineering.;Computer science.
学位 Ph.D.
年度 2018
页码 137 p.
总页数 137
原文格式 PDF
正文语种 eng
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专利

1. Evaluating the performance of a deep learning‐based computer‐aided diagnosis (DL‐CAD) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists [J] . Li Li, Zhou Liu, Hua Huang, Thoracic cancer. . 2019,第1期

机译：评估基于深度学习的计算机辅助诊断（DL-CAD）系统进行检测和表征肺结节的性能：与放射科医生双读数的性能进行比较
2. Radiologists with and without deep learning-based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses [J] . Wataya Tomohiro, Yanagawa Masahiro, Tsubamoto MitsukoSato TomoharuNishigaki DaikiKita KosukeYamagata KazukiSuzuki YukiHata AkinoriKido ShojiTomiyama NoriyukiOsaka Univ Reading Team European radiology. . 2023,第1期

机译：有和没有基于深度学习的计算机辅助诊断的放射科医生：表征和诊断肺结节/肿块的性能比较和观察者间一致性
3. Improving differential diagnosis of pulmonary large cell neuroendocrine carcinoma and small cell lung cancer via a transcriptomic, biological pathway-based machine learning model [J] . Junhong Guo, Likun Hou, Wei Zhang, Translational Oncology . 2021,第12期
4. Comparative Evaluation of Physicians' Pulmonary Nodule Diagnosis with Thin and Thick Section Muitislice CT Images at Lung Cancer Screening [C] . M.Sinsuat, M.Kaneko, K.Tominaga, 2008国际生物生理工程研讨会（The International Symposium on Biological and Physiological Engineering/The 22nd SICE Symposium on Biological and Physiological Engineering）论文集 . 2008

机译：薄层和厚层Muitislice CT图像在肺癌筛查中对医师肺结节诊断的比较评价

Characterizing Pulmonary Nodules using Machine and Deep Learning Methods to Improve Lung Cancer Diagnosis

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