Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT

机译：卷积神经网络有望在基线FDG-PET / CT上对肺癌T参数进行评估

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摘要

Aim To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images.

机译：目的开发基于卷积神经网络（CNN）的算法，用于在分期氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）/ CT图像上将肺癌病变分类为T1-T2或T3-T4。

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期刊名称 Contrast Media Molecular Imaging
作者
Margarita Kirienko; Martina Sollini; Giorgia Silvestri; Serena Mognetti; Emanuele Voulaz; Lidija Antunovic; Alexia Rossi; Luca Antiga; Arturo Chiti;
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年(卷),期 2018(2018),-1
年度 2018
页码 1382309
总页数 6
原文格式 PDF
正文语种
中图分类放射医学;影像诊断学;
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专利

1. Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT [J] . Margarita Kirienko, Martina Sollini, Giorgia Silvestri, Contrast media & molecular imaging . 2018,第4期

机译：基于基线FDG-PET / CT的肺癌T参数评估中的卷积神经网络
2. Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline [J] . Bonavita Ilaria, Rafael-Palou Xavier, Ceresa Mario, Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice . 2020,第期

机译：肺结结性肺结结性恶性评估的卷积神经网络整合肺癌分类管道
3. Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks [J] . Biocybernetics and biomedical engineering . 2020,第1期

机译：利用卷积神经网络利用最小冗余最大关联特征选择方法检测胸部CT图像的肺癌
4. Early Assessment of the Added Value of FDG-PET/CT and MRI-DWI in Monitoring Treatment Response of Stage III Non-Small Cell Lung Cancer [C] . K.R. Siegersma, J.M. Hummel, H. Broekhuizen Mediterranean Conference on Medical and Biological Engineering and Computin . 2016

机译：早期评估FDG-PET / CT和MRI-DWI的增加值监测III期非小细胞肺癌的治疗响应
5. A Bayesian Convolutional Neural Network Based Classifier to Detect Breast Cancer from Histopathological Images and Uncertainty Quantification [D] . ?Khairnar, Pushkar 2020

机译：基于贝叶斯卷积神经网络的分类器，检测组织病理学图像和不确定性量化的乳腺癌
6. Tumor Segmentation and Feature Extraction from Whole-Body FDG-PET/CT Using Cascaded 2D and 3D Convolutional Neural Networks [O] . Skander Jemaa, Jill Fredrickson, Richard A. D. Carano, 2020

机译：使用级联2D和3D卷积神经网络从全体FDG-PET / CT中肿瘤分割和特征提取
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

Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT

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