Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

首页> 外文期刊>Medical image analysis >Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

【24h】

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

机译：使用2D视图和开箱即用的卷积神经网络集成在计算机断层摄影中对肺周围裂节结节进行自动分类

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts. (C) 2015 Elsevier B.V. All rights reserved.

机译：在本文中，我们解决了肺裂周围结节（PFNs）的自动分类问题。分类问题被表述为一种机器学习方法，其中将检测到的结节候选分类为PFN或非PFN。使用监督学习，其中训练分类器以标记检测到的结节。 3D结节的分类被公式化为分类器的整体，这些分类器经过训练可以根据结节的2D视图识别PFN。为了在2D视图中描述结节形态，我们使用称为OverFeat的预训练卷积神经网络的输出。我们将我们的方法与最近提出的肺结节形态描述符（即袋频率）进行了比较，并说明了这两种策略所提供的优势，实现了AUC = 0.868的性能，接近人类专家之一。（C）2015 Elsevier B.V.保留所有权利。

著录项

来源
《Medical image analysis》 |2015年第null期|共8页
作者

展开▼
作者单位

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类影像诊断学;
关键词
Chest CT; Peri-fissural nodules; Lung cancer screening; Convolutional neural networks; OverFeat; Deep learning;

机译：胸部CT;胸膜结节结节;肺癌筛查;卷积神经网络;壮举;深度学习;

相似文献

外文文献
中文文献
专利

1. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box [J] . Medical image analysis . 2015,第Null期

机译：使用2D视图和开箱即用的卷积神经网络集成在计算机断层摄影中对肺周围裂节结节进行自动分类
2. 3D Automatic Segmentation of Aortic Computed Tomography Angiography Combining Multi-View 2D Convolutional Neural Networks [J] . Alice Fantazzini, Mario Esposito, Alice Finotello, Cardiovascular engineering and technology . 2020,第5期

机译：三维计算机断层造影血管造影的自动分割结合多视图2D卷积神经网络
3. Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification [J] . Dai Yaojun, Yan Shiju, Zheng Bin, Physics in medicine and biology. . 2018,第24期

机译：将自动学习的肺结核属性纳入卷积神经网络，以提高良性恶性结节分类的准确性
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. Automatic segmentation of small pulmonary nodules in computed tomography data using a radial basis function neural network with application to volume estimation [D] . Tuinstra, Timothy Ryan 2008

机译：使用径向基函数神经网络的计算机断层扫描数据中的小肺结节自动分割及其在体积估计中的应用
6. Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks [O] . Yuya Onishi, Atsushi Teramoto, Masakazu Tsujimoto, 2006

机译：使用由生成对抗网络训练的深卷积神经网络在计算机断层扫描图像中自动进行肺结节分类
7. Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks [O] . Yuya Onishi, Atsushi Teramoto, Masakazu Tsujimoto, 2019

机译：使用由生成的对抗网络训练的深卷积神经网络在计算机断层扫描图像中自动肺结核分类

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

摘要

著录项

相似文献

相关主题

期刊订阅