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The Gap in the Thickness: Estimating Effectiveness of Pulmonary Nodule Detection in Thick- and Thin-Section CT Images with 3D Deep Neural Networks

  • Published:2023-02 Issue: Volume:229 Page:107290
  • ISSN:0169-2607
  • Container-title:Computer Methods and Programs in Biomedicine
  • language:en
  • Short-container-title:Computer Methods and Programs in Biomedicine

Author:

Guo Quan,Wang Chengdi,Guo Jixiang,Bai Hongli,Xu Xiuyuan,Yang Lan,Wang Jianyong,Chen Nan,Wang Zihuai,Gan Yuncui,Liu Lunxu,Li Weimin,Yi Zhang

Funder

National Key Research and Development Program of China

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications,Software

Reference23 articles.

1. Cancer incidence and mortality worldwide: sources, methods and major patterns in globocan 2012;Ferlay;International Journal of Cancer,2015

2. Reducing the dimensionality of data with neural networks;Hinton;Science,2006

3. Speech recognition with deep recurrent neural networks;Graves,2013

4. Imagenet classification with deep convolutional neural networks;Krizhevsky,2012

5. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs;V;JAMA,2016

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