首页> 外文会议>SPIE Medical Imaging Conference >Preliminary study of benign and malignant differentiation of small pulmonary nodules in Lung CT images by Using Deep Learning Convolutional Neural Network

【24h】

Preliminary study of benign and malignant differentiation of small pulmonary nodules in Lung CT images by Using Deep Learning Convolutional Neural Network

机译：深受深层学习卷积神经网络促进肺CT图像小肺结节良恶性分化的初步研究

获取原文

页面导航

摘要
著录项
相似文献
相关主题

摘要

The benign and malignant differential diagnosis of small pulmonary nodules (diameter < 20 mm) found in lung CT images is big challenges for most of radiologists. Here, we presented our preliminary study of benign and malignant differentiation of small pulmonary nodules in lung CT images by using deep learning Convolutional Neural Network (CNN). The 921 cases with small benign and malignant pulmonary nodules confirmed by pathology were collected from three data sources and were used to train and validate the CNN. The preliminary results of AUCs of ROC curves for differentiating benign and malignant pulmonary small nodules with various types and sizes of solid, semi-solid and ground glass nodules were presented and discussed.

机译：在肺CT图像中发现的小肺结核（直径<20mm）的良性和恶性鉴别诊断是大多数放射科医生的大挑战。在这里，我们通过使用深入学习卷积神经网络（CNN）介绍了对肺CT图像中小肺结核的良性和恶性分化的初步研究。从三种数据来源收集通过病理学证实的小良性和恶性肺结核的921例，并用于培训并验证CNN。提出并讨论了具有各种类型和尺寸的固体，半固体和覆盖玻璃结节的良性和恶性肺部小结节的ROC曲线AUC的初步结果。

著录项

来源
《SPIE Medical Imaging Conference》|2018年|v. p.|共11页
会议地点
作者
Yangfan Ni; Haozhe Huang; Dezhong Zheng; Jianyong Sun; Yuanyuan Yang; Mingqing Wang; Yiping Gu; Ming Li; Guozhen Zhang; Wentao Li; Jianguo Zhang;
展开▼
作者单位

展开▼
会议组织
原文格式 PDF
正文语种
中图分类计算技术、计算机技术;
关键词
Convolutional Neural Network; pulmonary nodules; Tensorflow; medical images;

机译：卷积神经网络;肺结核;Tensorflow;医学图像;

相似文献

外文文献
中文文献
专利

1. Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT [J] . Radiology . 2020,第2期

机译：利用卷积神经网络来确定胸部CT的体积变化的态度从恶性肺结结分化
2. Mobile health framework based on adaptive feature selection of deep convolutional neural network and QoS optimisation for benign-malignant lung nodule classification [J] . Xiao Wang, Huiming Gao, Juanjuan Zhao, International journal of wireless and mobile computing . 2019,第3期

机译：基于自适应特征选择的移动健康框架，对深卷积神经网络和QoS优化对良性恶性肺结节分类
3. Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network [J] . Yi-Ming Xu, Teng Zhang, Hai Xu, Cancer Management and Research . 2020,第4期

机译：CT图像深度学习图像：使用卷积神经网络进行自动肺结核检测，用于随后的管理
4. Preliminary study of benign and malignant differentiation of small pulmonary nodules in Lung CT images by Using Deep Learning Convolutional Neural Network [C] . Yangfan Ni, Haozhe Huang, Dezhong Zheng, SPIE Medical Imaging Conference . 2018

机译：深度学习卷积神经网络对肺部CT图像中小肺结节良恶性分化的初步研究
5. Deep Learning Convolutional Neural Network Applications in Image Analysis to Identify Defects [D] . Umakanth, Srijith Parakkat 2018

机译：深度学习卷积神经网络在图像分析中识别缺陷的应用
6. Computer-aided diagnosis of lung nodule classification between benign nodule primary lung cancer and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning [O] . Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, -1

机译：使用转移学习的深度卷积神经网络对不同图像大小的良性结节原发性肺癌和转移性肺癌之间的计算机辅助诊断
7. Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning [O] . Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, 2018

机译：计算机辅助诊断良性结节，原发性肺癌和转移肺癌在不同图像大小与转移学习的不同图像尺寸

Preliminary study of benign and malignant differentiation of small pulmonary nodules in Lung CT images by Using Deep Learning Convolutional Neural Network

摘要

著录项

相似文献

相关主题

期刊订阅