首页> 外文学位 >Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning

【24h】

Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning

机译：利用深度学习，从计算机断层扫描图像的肺结死恶性预测

获取原文

获取原文并翻译 | 示例

页面导航

著录项
相似文献
相关主题

著录项

作者
Paul, Rahul.;
展开▼
作者单位

University of South Florida.;

展开▼
授予单位 University of South Florida.;
学科
学位 Ph.D.
年度 2020
页码 150 p.
总页数 150
原文格式 PDF
正文语种 eng
中图分类 AAI27738591;
关键词
Convolutional neural network; Deep Features; Lung cancer; Radiomics;

机译：卷积神经网络;深度特征;肺癌;辐射瘤;

相似文献

外文文献
中文文献
专利

1. Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks [J] . da Nobrega Raul Victor M., Reboucas Filho Pedro P., Rodrigues Murillo B., Neural computing & applications . 2020,第15期

机译：使用转移学习和卷积神经网络的胸部计算断层扫描图像中的肺结结恶性分类
2. Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images [J] . Song QingZeng, Zhao Lei, Luo XingKe, Journal of healthcare engineering. . 2017,第1期

机译：使用深度学习对计算机断层扫描图像上的肺结节进行分类
3. Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images [J] . QingZeng Song, Lei Zhao, XingKe Luo, Journal of healthcare engineering. . 2017,第1期

机译：利用深入学习对计算机断层摄影图像肺结节分类
4. Classification of Lung Nodule Malignancy Risk on Computed Tomography Images Using Convolutional Neural Network: A Comparison Between 2D and 3D Strategies [C] . Xingjian Yan, Jianing Pang, Hang Qi, Asian conference on computer vision;Workshop on new trends in image restoration and enhancement;Workshop on assistive vision;ACCV workshop on hyperspectral image and signal processing;Workshop on computer vision technologies for smart vehicle;Workshop on spontaneous facial behavior analysis;Workshop on 3D modelling and applications;ACCV workshop on e-heritage;Workshop on multiview lip-reading challenges;Workshop on facial informatics;Workshop on discrete geometry and mathematical morphology for computer vision;Workshop on mathematical and computational methods in biomedical imaging and image analysis;International workshop on driver drowsiness detection from video;Workshop on meeting HCI with CV;Workshop on human identification for surveillance methods and applications;Workshop on benchmark and evaluation of surveillance task;Workshop on computer vision for affective computing;Workshop on interpretation and visualization of deep neural nets . 2017

机译：使用卷积神经网络对计算机断层扫描图像上的肺结节恶性风险进行分类：2D和3D策略之间的比较
5. Change Descriptors for Determining Nodule Malignancy in Lung CT Screening Images [D] . Geiger, Benjamin 2019

机译：用于确定肺部CT筛查图像中结节恶性肿瘤的变化描述符
6. Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images [O] . QingZeng Song, Lei Zhao, XingKe Luo, 2017

机译：使用深度学习对计算机断层扫描图像上的肺结节进行分类
7. Using Deep Learning for Classification of Early Lung Nodules on Computed Tomography Images [O] . Lucas Lima, Marcelo Oliveira 2018

机译：利用深入学习对计算机断层扫描图像的早期肺结节分类

Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning

著录项

相似文献

相关主题

期刊订阅