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Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks

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

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Yuya Onishi; Atsushi Teramoto; Masakazu Tsujimoto; Tetsuya Tsukamoto; Kuniaki Saito; Hiroshi Toyama; Kazuyoshi Imaizumi; Hiroshi Fujita;
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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. Automated pulmonary nodule detection in CT images using deep convolutional neural networks [J] . Xie Hongtao, Yang Dongbao, Sun Nannan, Pattern Recognition: The Journal of the Pattern Recognition Society . 2019,第期

机译：使用深卷积神经网络的CT图像中自动肺结核检测
3. Lung Nodule Diagnosis on 3D Computed Tomography Images Using Deep Convolutional Neural Networks [J] . Qianqian Zhang, Haifeng Wang, Sang Won Yoon, Procedia Manufacturing . 2019,第186期

机译：利用深卷积神经网络对3D计算机断层扫描图像进行肺结核诊断
4. Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks [C] . Jia Ding, Aoxue Li, Zhiqiang Hu, International conference on medical imaging computing and computer-assisted intervention . 2017

机译：使用深度卷积神经网络在计算机断层扫描图像中进行准确的肺结节检测
5. Multi-Scale Convolutional Neural Networks for Segmentation of Pulmonary Structures in Computed Tomography [D] . Gerard, Sarah E. 2018

机译：用于计算机断层扫描中肺结构分割的多尺度卷积神经网络
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. Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks [O] . Ding, Jia, Li, Aoxue, Hu, Zhiqiang, 2017

机译：计算机断层扫描图像中准确的肺结节检测深度卷积神经网络

Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks

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