A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification-Reference-Cited by-同舟云学术

A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification

Published:2019-11-25 Issue:2 Volume:15 Page:287-295
ISSN:1861-6410
Container-title:International Journal of Computer Assisted Radiology and Surgery
language:en
Short-container-title:Int J CARS

Author:

Ren Ying,Tsai Min-Yu,Chen Liyuan,Wang Jing,Li Shulong,Liu Yufei,Jia Xun,Shen Chenyang^ORCID

Funder

Key Technologies Research and Development Program

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Radiology Nuclear Medicine and imaging,General Medicine,Surgery,Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11548-019-02097-8.pdf

Reference31 articles.

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2. Lee SLA, Kouzani AZ, Hu EJ (2010) Random forest based lung nodule classification aided by clustering. Comput Med Imag Graph 34:535–542

3. Zhang F, Song Y, Cai W, Lee M-Z, Zhou Y, Huang H, Shan S, Fulham MJ, Feng DD (2014) Lung nodule classification with multilevel patch-based context analysis. IEEE Trans Biomed Eng 61:1155–1166

4. Shen W, Zhou M, Yang F, Yang C, Tian J (2015) Multi-scale convolutional neural networks for lung nodule classification. In: International conference on information processing in medical imaging. Springer, pp 588–599

5. Kumar D, Wong A, Clausi DA (2015) Lung nodule classification using deep features in CT images. In: 2015 12th conference on computer and robot vision. IEEE, pp 133–138

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