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Volumn 7, Issue , 2017, Pages

Corrigendum: Towards automatic pulmonary nodule management in lung cancer screening with deep learning (Scientific Reports (2017) 7 (46479) DOI: 10.1038/srep46479);Towards automatic pulmonary nodule management in lung cancer screening with deep learning

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSTIC IMAGING; EARLY CANCER DIAGNOSIS; HUMAN; LUNG NODULE; LUNG TUMOR; PROCEDURES; X-RAY COMPUTED TOMOGRAPHY;

EID: 85017644487     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep46878     Document Type: Erratum
Times cited : (312)

References (34)
  • 1
    • 79961108629 scopus 로고    scopus 로고
    • Reduced lung-cancer mortality with low-dose computed tomographic screening
    • Aberle, D. R., et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine 365, 395-409 (2011).
    • (2011) New England Journal of Medicine , vol.365 , pp. 395-409
    • Aberle, D.R.1
  • 2
    • 84908573929 scopus 로고    scopus 로고
    • Benefits and harms of computed tomography lung cancer screening strategies: A comparative modeling study for the U. S. Preventive services task force
    • de Koning, H. J., et al. Benefits and harms of computed tomography lung cancer screening strategies: A comparative modeling study for the U. S. preventive services task force. Annals of Internal Medicine (2013).
    • (2013) Annals of Internal Medicine
    • De Koning, H.J.1
  • 3
    • 77951647196 scopus 로고    scopus 로고
    • A new computationally efficient CAD system for pulmonary nodule detection in CT imagery
    • Messay, T., Hardie, R. C., Rogers, S. K. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Medical Image Analysis 14, 390-406 (2010).
    • (2010) Medical Image Analysis , vol.14 , pp. 390-406
    • Messay, T.1    Hardie, R.C.2    Rogers, S.K.3
  • 4
    • 84892452298 scopus 로고    scopus 로고
    • Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images
    • Jacobs, C., et al. Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Medical Image Analysis 18, 374-384 (2014).
    • (2014) Medical Image Analysis , vol.18 , pp. 374-384
    • Jacobs, C.1
  • 5
    • 84941072949 scopus 로고    scopus 로고
    • Automatic detection of large pulmonary solid nodules in thoracic CT images
    • Setio, A. A. A., Jacobs, C., Gelderblom, J., van Ginneken, B. Automatic detection of large pulmonary solid nodules in thoracic CT images. Medical Physics 42, 5642-5653 (2015).
    • (2015) Medical Physics , vol.42 , pp. 5642-5653
    • Setio, A.A.A.1    Jacobs, C.2    Gelderblom, J.3    Van Ginneken, B.4
  • 6
    • 84968638584 scopus 로고    scopus 로고
    • Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks
    • Setio, A. A. A., et al. Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks. IEEE Transactions on Medical Imaging 35, 1160-1169 (2016).
    • (2016) IEEE Transactions On Medical Imaging , vol.35 , pp. 1160-1169
    • Setio, A.A.A.1
  • 7
    • 84883373200 scopus 로고    scopus 로고
    • Probability of cancer in pulmonary nodules detected on first screening CT
    • McWilliams, A., et al. Probability of cancer in pulmonary nodules detected on first screening CT. New England Journal of Medicine 369, 910-919 (2013).
    • (2013) New England Journal of Medicine , vol.369 , pp. 910-919
    • McWilliams, A.1
  • 8
    • 84867911435 scopus 로고    scopus 로고
    • Pulmonary perifissural nodules on CT scans: Rapid growth is not a predictor of malignancy
    • de Hoop, B., van Ginneken, B., Gietema, H., Prokop, M. Pulmonary perifissural nodules on CT scans: Rapid growth is not a predictor of malignancy. Radiology 265, 611-616 (2012).
    • (2012) Radiology , vol.265 , pp. 611-616
    • De Hoop, B.1    Van Ginneken, B.2    Gietema, H.3    Prokop, M.4
  • 9
    • 0036109223 scopus 로고    scopus 로고
    • CT screening for lung cancer: Frequency and significance of part-solid and nonsolid nodules
    • Henschke, C. I., et al. CT screening for lung cancer: Frequency and significance of part-solid and nonsolid nodules. American Journal of Roentgenology 178, 1053-1057 (2002).
    • (2002) American Journal of Roentgenology , vol.178 , pp. 1053-1057
    • Henschke, C.I.1
  • 10
    • 84948732335 scopus 로고    scopus 로고
    • Observer variability for classification of pulmonary nodules on low-dose CT images and its effect on nodule management
    • van Riel, S. J., et al. Observer variability for classification of pulmonary nodules on low-dose CT images and its effect on nodule management. Radiology 277, 863-871 (2015).
    • (2015) Radiology , vol.277 , pp. 863-871
    • Van Riel, S.J.1
  • 11
    • 84923650021 scopus 로고    scopus 로고
    • Solid, part-solid, or non-solid: Classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system
    • Jacobs, C., et al. Solid, part-solid, or non-solid: Classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system. Investigative Radiology 50, 168-173 (2015).
    • (2015) Investigative Radiology , vol.50 , pp. 168-173
    • Jacobs, C.1
  • 13
    • 84926435768 scopus 로고    scopus 로고
    • Bag of frequencies: A descriptor of pulmonary nodules in computed tomography images
    • Ciompi, F., et al. Bag of frequencies: A descriptor of pulmonary nodules in computed tomography images. IEEE Transactions on Medical Imaging 34, 1-12 (2015).
    • (2015) IEEE Transactions On Medical Imaging , vol.34 , pp. 1-12
    • Ciompi, F.1
  • 14
    • 84948798628 scopus 로고    scopus 로고
    • Automatic detection of spiculation of pulmonary nodules in computed tomography images
    • Ciompi, F., et al. Automatic detection of spiculation of pulmonary nodules in computed tomography images. In Medical Imaging, of Proceedings of the SPIE vol. 9414, (2015).
    • (2015) Medical Imaging of Proceedings of the SPIE , vol.9414
    • Ciompi, F.1
  • 15
    • 84943752367 scopus 로고    scopus 로고
    • Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box
    • Ciompi, F., et al. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Medical Image Analysis 26, 195-202 (2015).
    • (2015) Medical Image Analysis , vol.26 , pp. 195-202
    • Ciompi, F.1
  • 17
    • 84910651844 scopus 로고    scopus 로고
    • Deep learning in neural networks: An overview
    • Schmidhuber, J. Deep learning in neural networks: An overview. Neural Networks 61, 85-117 (2015).
    • (2015) Neural Networks , vol.61 , pp. 85-117
    • Schmidhuber, J.1
  • 18
  • 20
    • 85083951635 scopus 로고    scopus 로고
    • OverFeat: Integrated recognition, localization and detection using convolutional networks
    • ArXiv: 1312. 6229
    • Sermanet, P., et al. OverFeat: Integrated recognition, localization and detection using convolutional networks. In International Conference on Learning Representations (ICLR 2014) ArXiv: 1312. 6229 (2014).
    • (2014) International Conference On Learning Representations (ICLR 2014)
    • Sermanet, P.1
  • 21
    • 84941122549 scopus 로고    scopus 로고
    • Going deeper with convolutions
    • Szegedy, C., et al. Going deeper with convolutions. arXiv:14094842v1 (2014).
    • (2014) ArXiv:14094842v1
    • Szegedy, C.1
  • 25
    • 84859295557 scopus 로고    scopus 로고
    • Annual or biennial CT screening versus observation in heavy smokers: 5-year results of the MILD trial
    • Pastorino, U., et al. Annual or biennial CT screening versus observation in heavy smokers: 5-year results of the MILD trial. European Journal of Cancer Prevention 21, 308-315 (2012).
    • (2012) European Journal of Cancer Prevention , vol.21 , pp. 308-315
    • Pastorino, U.1
  • 26
    • 67651159241 scopus 로고    scopus 로고
    • The Danish randomized lung cancer CT screening trial-overall design and results of the prevalence round
    • Pedersen, J. H., et al. The Danish randomized lung cancer CT screening trial-overall design and results of the prevalence round. Journal of Thoracic Oncology 4, 608-614 (2009).
    • (2009) Journal of Thoracic Oncology , vol.4 , pp. 608-614
    • Pedersen, J.H.1
  • 27
    • 33645697952 scopus 로고    scopus 로고
    • Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans
    • Kuhnigk, J. M., et al. Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Transactions on Medical Imaging 25, 417-434 (2006).
    • (2006) IEEE Transactions On Medical Imaging , vol.25 , pp. 417-434
    • Kuhnigk, J.M.1
  • 28
    • 84941425888 scopus 로고    scopus 로고
    • Predictive accuracy of the pancan lung cancer risk prediction model-external validation based on CT from the danish lung cancer screening trial
    • Winkler Wille, M. M., et al. Predictive accuracy of the pancan lung cancer risk prediction model-external validation based on CT from the danish lung cancer screening trial. European Radiology 25, 3093-3099 (2015).
    • (2015) European Radiology , vol.25 , pp. 3093-3099
    • Winkler Wille, M.M.1
  • 30
    • 84933585162 scopus 로고    scopus 로고
    • Very deep convolutional networks for large-scale image recognition
    • Simonyan, K., Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv:14091556 (2014).
    • (2014) ArXiv: 14091556
    • Simonyan, K.1    Zisserman, A.2
  • 33
    • 85083951076 scopus 로고    scopus 로고
    • ADAM: A method for stochastic optimization
    • Kingma, D., Ba, J. ADAM: A method for stochastic optimization. arXiv:14126980 (2015).
    • (2015) ArXiv: 14126980
    • Kingma, D.1    Ba, J.2
  • 34
    • 80053446757 scopus 로고    scopus 로고
    • An analysis of single-layer networks in unsupervised feature learning
    • Coates, A., Lee, H., Ng, A. Y. An analysis of single-layer networks in unsupervised feature learning. In Aistats (2011).
    • (2011) Aistats
    • Coates, A.1    Lee, H.2    Ng, A.Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.