|
1
|
Bray F, Ferlay J, Soerjomataram I, Siegel
RL, Torre LA and Jemal A: Global cancer statistics 2018: GLOBOCAN
estimates of incidence and mortality worldwide for 36 cancers in
185 countries. CA Cancer J Clin. 68:394–424. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Rzechonek A, Grzegrzolka J, Blasiak P,
Ornat M, Piotrowska A, Nowak A and Dziegiel P: Correlation of
expression of tenascin C and blood vessel density in non-small cell
lung cancers. Anticancer Res. 38:1987–1991. 2018.PubMed/NCBI
|
|
3
|
Chen S, Harmon S, Perk T, Li X, Chen M, Li
Y and Jeraj R: Diagnostic classification of solitary pulmonary
nodules using dual time 18F-FDG PET/CT image texture
features in granuloma-endemic regions. Sci Rep. 7:93702017.
View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Dai M, Qi J, Zhou Z and Gao F: The
classification of pulmonary nodules based on texture features over
local jet transformation space. Chin J Biomed Eng. 36:12–19.
2017.
|
|
5
|
Felix A, Oliveira M, Machado A and Raniery
J: Using 3D texture and margin sharpness features on classification
of small pulmonary nodules. In: Proceedings of 29th Conference on
Graphics. (Patterns and Images (SIBGRAPI), Sao Paulo). 394–400.
2016.
|
|
6
|
Song J, Hui L, Geng F and Zhang C:
Weakly-supervised classification of pulmonary nodules based on
shape characters. In: Proceedings of 2016 IEEE 14th Intl Conf on
Dependable, Autonomic and Secure Computing, 14th Intl Conf on
Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data
Intelligence and Computing and Cyber Science and Technology
Congress (DASC/PiCom/DataCom/CyberSciTech). (Auckland). 228–232.
2016.
|
|
7
|
Niehaus R, Raicu DS, Furst J and Armato S
III: Toward understanding the size dependence of shape features for
predicting spiculation in lung nodules for computer-aided
diagnosis. J Digit Imaging. 28:704–717. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Dhara AK, Mukhopadhyay S, Dutta A, Garg M
and Khandelwal N: A Combination of shape and texture features for
classification of pulmonary nodules in lung CT images. J Digit
Imaging. 29:466–475. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Li W, Cao P, Zhao D and Wang J: Pulmonary
nodule classification with deep convolutional neural networks on
computed tomography images. Comput Math Methods Med.
2016:62150852016. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Tartar A, Akan A and Kilic N: A novel
approach to malignant-benign classification of pulmonary nodules by
using ensemble learning classifiers. Conf Proc IEEE Eng Med Biol
Soc. 2014:4651–4654. 2014.PubMed/NCBI
|
|
11
|
Nibali A, Zhen H and Wollersheim D:
Pulmonary nodule classification with deep residual networks. Int J
Comput Assist Radiol Surg. 12:1799–1808. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Shen W, Zhou M, Yang F, Yang C and Tian J:
Multi-scale convolutional neural networks for lung nodule
classification. Inf Process Med Imaging. 24:588–599.
2015.PubMed/NCBI
|
|
13
|
Kumar D, Wong A and Clausi DA: Lung nodule
classification using deep features in CT images. In: Proceedings of
the 2015 12th Conference on Computer and Robot Vision. (Halifax,
Canada. IEEE). 133–138. 2015.
|
|
14
|
Kaya A and Can AB: A weighted rule based
method for predicting malignancy of pulmonary nodules by nodule
characteristics. J Biomed Inform. 56:69–79. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Li G, Kim H, Tan JK, Ishikawa S, Hirano Y,
Kido S and Tachibana R: Semantic characteristics prediction of
pulmonary nodule using artificial neural networks. Conf Proc IEEE
Eng Med Biol Soc. 2013:5465–5468. 2013.PubMed/NCBI
|
|
16
|
Chen S, Ni D, Qin J, Lei B, Wang T and
Cheng JZ: Bridging computational features toward multiple semantic
features with multi-task regression: A study of ct pulmonary
nodules. International Conference on Medical Image Computing and
Computer-Assisted Intervention. Springer. (Cham). 53–60. 2016.
|
|
17
|
Shewaye TN and Mekonnen AA:
Benign-malignant lung nodule classification with geometric and
appearance histogram features. arXiv: Computer Vision and Pattern
Recognition. (arXiv:1605.08350v1 [cs.CV]). 2016.
|
|
18
|
Orozco HM, Villegas OOV, de Jesús Ochoa
Domínguez O and Sánchez VGC: Lung nodule classification in CT
thorax images using support vector machines. Mexican International
Conference on Artificial Intelligence. IEEE. 277–283. 2014.
|
|
19
|
Zhao A, Qi L, Li J, Dong J and Yu H: LSTM
for diagnosis of neurodegenerative diseases using gait data. In:
Proceedings of the 9th International Conference on Graphics and
Image Processing. SPIE Press. 2018.
|
|
20
|
Jacobs C, van Rikxoort EM, Twellmann T,
Scholten ET, de Jong PA, Kuhnigk JM, Oudkerk M, de Koning HJ,
Prokop M, Schaefer-Prokop C and van Ginneken B: Automatic detection
of subsolid pulmonary nodules in thoracic computed tomography
images. Med Image Anal. 18:374–384. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Ma J, Wang Q, Ren Y, Hu H and Zhao J:
Automatic lung nodule classification with radiomics approach.
Medical Imaging 2016: PACS and Imaging Informatics: Next Generation
and Innovations. 9789:SPIE Proceedings. 2016.
|
|
22
|
Armato SG III, McLennan G, Bidaut L,
McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke
CI, Hoffman EA, et al: The lung image database consortium (LIDC)
and image database resource initiative (IDRI): A completed
reference database of lung nodules on CT scans. Med Phys.
38:915–931. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Chen M, Weinberger KQ, Sha F and Bengio
YO: Marginalized denoising auto-encoders for nonlinear
representations. Proceedings of the 31st International Conference
on Machine Learning. PMLR. 32:1476–1484. 2014.
|
|
24
|
Szegedy C, Liu W, Jia Y, Sermanet P, Reed
S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A: Going deeper
with convolutions. arXiv: Computer Vision and Pattern Recognition.
(arXiv:1409.4842v1 [cs.CV]). 2015. View Article : Google Scholar
|
|
25
|
Simonyan K and Zisserman A: Very deep
convolutional networks for large-scale image recognition. arXiv:
Computer Vision and Pattern Recognition arXiv:1409.1556v6 [cs.CV].
2014.
|
|
26
|
Hu J, Shen L, Albanie S, Sun G and Wu E:
Squeeze-and-excitation networks. arXiv: Computer Vision and Pattern
Recognition. (arXiv:1709.01507v4 [cs.CV]). 2017.
|
|
27
|
He K, Zhang X, Ren S and Sun J: Deep
residual learning for image recognition. IEEE Conference on
Computer Vision and Pattern Recognition. 770–778. 2016.
|
|
28
|
Haralick RM, Shanmugam K and Dinstein IH:
Textural features for image classification. IEEE Transactions on
Systems. (Man, and Cybernetics. Vol SMC-3. IEEE). 610–621.
1973.
|
|
29
|
Pan L, Qiang Y, Yuan J and Wu L: Rapid
retrieval of lung nodule CT images based on hashing and pruning
methods. Biomed Res Int. 2016:31626492016. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Li X, Yang Y, Xiong H, Song S and Jia H:
Pulmonary nodules detection algorithm based on robust cascade
classifier for CT images. Control and Decision Conference. IEEE.
231–235. 2017.
|
|
31
|
Zinovev D, Furst J and Raicu D: Building
an ensemble of probabilistic classifiers for lung nodule
interpretation. Proceedings of the 10th International Conference on
Machine Learning and Applications and Workshops. IEEE Computer
Society. 155–161. 2011.
|
|
32
|
Zou KH, O'Malley AJ and Mauri L:
Receiver-operating characteristic analysis for evaluating
diagnostic tests and predictive models. Circulation. 115:654–657.
2007. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Zinovev D, Feigenbaum J, Furst J and Raicu
D: Probabilistic lung nodule classification with belief decision
trees. Conf Proc IEEE Eng Med Biol Soc. 2011:4493–4498.
2011.PubMed/NCBI
|
|
34
|
Shen W, Zhou M, Yang F, Yu D, Dong D, Yang
C, Zang Y and Tian J: Multi-crop Convolutional Neural Networks for
lung nodule malignancy suspiciousness classification. Pattern
Recogn. 61:663–673. 2017. View Article : Google Scholar
|
|
35
|
Rodrigues MB, Da NóBrega RVM, Alves SSA,
Filho PPR, Duarte JBF, Sangaiah AK and De Albuquerque VHC: Health
of things algorithms for malignancy level classification of lung
nodules. IEEE Access. 6:18592–18601. 2018. View Article : Google Scholar
|
|
36
|
Sun W, Huang X, Tseng TL, Zhang J and Qian
W: Computerized lung cancer malignancy level analysis using 3D
texture features. Medical Imaging 2016: PACS and Imaging
Informatics: Next Generation and Innovations. 9785:SPIE
Proceedings. 2016.
|
|
37
|
Seo N, Seok J, Lim S and Cho A: Radiologic
diagnosis (CT, MRI, & PET-CT). Surg Gastric Cancer. 67–86.
2019. View Article : Google Scholar
|
|
38
|
Oliva MR and Saini S: Liver cancer
imaging: Role of CT, MRI, US and PET. Cancer Imaging. 4:S42–S46.
2004. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Muhammad MN, Raicu DS, Furst JD and
Varutbangkul E: Texture versus shape analysis for lung nodule
similarity in computed tomography studies. Medical Imaging 2008:
PACS and Imaging Informatics. 6919:SPIE Proceedings. 2008.
|
|
40
|
Wormanns D, Fiebich M, Saidi M, Diederich
S and Heindel W: Automatic detection of pulmonary nodules at spiral
CT: Clinical application of a computer-aided diagnosis system. Eur
Radiol. 12:1052–1057. 2002. View Article : Google Scholar : PubMed/NCBI
|
