摘要
胸部CT扫描是肺癌早期筛查和诊断的主要检查手段,应用于胸部影像诊断领域的基于深度学习的计算机辅助诊断(CAD)系统可对CT图像上的肺结节进行检测和分类。深度学习技术可提高CAD系统的性能,尤其是在提高肺结节检测的准确率和降低假阳性率方面。笔者就CAD系统中的深度学习模型在肺结节中的应用现状和研究进展作一综述。
Chest CT scan is the primary medical imaging method performed for the early screening and diagnosis of lung cancer.Deep-learning based computer aided diagnosis(CAD)system for chest CT imaging is helpful for detecting and classifying pulmonary nodules.Deep-learning techniques can improve the performance of CAD systems,especially in enhancing the accuracy of pulmonary nodule detection and reducing false-positive rates.This article reviewed the current application status of deep-learning models in CAD systems and the progress that has been achieved in using these systems for imaging pulmonary nodules.
作者
刘婧
张莺
Liu Jing;Zhang Ying(Department of Nuclear Medicine,the Second Affiliated Hospital of Zhejiang University School of Medicine,Hangzhou 310009,China)
出处
《国际放射医学核医学杂志》
2020年第1期22-26,共5页
International Journal of Radiation Medicine and Nuclear Medicine
基金
国家重点研发计划项目(2016YFA0100900、2016YFA0100902)
国家自然科学基金(81501508)。
关键词
人工智能
肺肿瘤
诊断
计算机辅助
神经网络(计算机)
深度学习
Artificial intelligence
Lung neoplasms
Diagnosis
computer-assisted
Neural networks(computer)
Deep learning