Pulmonary nodule detection in CT scans with equivariant CNNs
Tài liệu tham khảo
Al Mohammad, 2017, A review of lung cancer screening and the role of computer-aided detection, Clin. Radiol., 72, 433, 10.1016/j.crad.2017.01.002
American Cancer Society, 2017. Lung cancer detection and early prevention.Last revised: February 22, 2016.
American Cancer Society Statistics Center, 2017. Lung cancer key statistics. Last update: January 2017.
Armato, 2004, Lung image database consortium research group. lung image database consortium: developing a resource for the medical imaging research community, Radiology, 232, 739, 10.1148/radiol.2323032035
Armato III, 2007, The lung image database consortium (lidc): an evaluation of radiologist variability in the identification of lung nodules on ct scans., Acad. Radiol., 14
Armato III, 2009, Assessment of radiologist performance in the detection of lung nodules: dependence on the definition of “truth”, Acad. Radiol., 16, 28, 10.1016/j.acra.2008.05.022
Bekkers, E. J., Lafarge, M. W., Veta, M., Eppenhof, K. A. J., Pluim, J. P. W., 2018. Roto-Translation covariant convolutional networks for medical image analysis. Unknown.
Bhargavan, 2009, Workload of radiologists in united states in 2006–2007 and trends since 1991–1992, Radiology, 252, 458, 10.1148/radiol.2522081895
Bogoni, 2012, Impact of a computer-aided detection (cad) system integrated into a picture archiving and communication system (pacs) on reader sensitivity and efficiency for the detection of lung nodules in thoracic ct exams., J. Digit. Imaging, 25, 10.1007/s10278-012-9496-0
Cohen, 2016, Group equivariant convolutional networks, 2990
Cohen, 2018, Spherical CNNs
Cohen, 2017, Steerable CNNS
Dieleman, S., Fauw, J., Kavukcuaglu, K., 2016. Exploiting cyclic symmetry in convolutional neural networks. Proceedings of the International Conference on Machine Learning, 1889–1898.
Firmino, 2014, Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects, Biomed. Eng. Online, 13, 10.1186/1475-925X-13-41
van Ginneken, 2017, Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning, Radiol. Phys. Technol., 10
Glorot, X., Bengio, Y., 2010. Understanding the difficulty of training deep feedforward neural networks. Proceedings of the International Conference on Artificial Intelligence and Statistics, 249–256.
Hansell, 2008, Fleischner society: glossary of terms for thoracic imaging, Radiology, 697, 10.1148/radiol.2462070712
Kingma, 2014, Adam: a method for stochastic optimization, CoRR
Kondor, R., Son, H. T., Pan, H., Anderson, B., Trivedi, S., 2018. Covariant compositional networks for learning graphs. unknown.
Lauritzen, 2016, Radiologist-initiated double reading of abdominal ct: retrospective analysis of the clinical importance of changes to radiology reports., BMJ Qual. Safety, 25, 595, 10.1136/bmjqs-2015-004536
McNitt-Gray, 2007, The lung image database consortium (LIDC) data collection process for nodule detection and annotation, Radiology, 14, 1464
National Lung Screening Trial, 2011, Reduced lung-cancer mortality with low-dose computed tomographic screening, N Top N. Engl. J. Med., 365, 395, 10.1056/NEJMoa1102873
Oudkerk, 2017, European position statement on lung cancer screening, Lancet Oncol., 18, 754, 10.1016/S1470-2045(17)30861-6
Rubin, 2005, Pulmonary nodules on multi–detector row ct scans: performance comparison of radiologists and computer-aided detection, Radiology, 234, 274, 10.1148/radiol.2341040589
Setio, 2016, Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge, CoRR
Simard, 2003, Best practices for convolutional neural networks applied to visual document analysis, 3, 958
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., Riley, P., 2018. Tensor field networks: rotation- and translation-equivariant neural networks for 3D point clouds.
Van Ginneken, 2010, Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the anode09 study, Med. Image Anal., 14, 707, 10.1016/j.media.2010.05.005
Wang, 2016, Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the global burden of disease study, Lancet, 388, 1459, 10.1016/S0140-6736(16)31012-1
Weiler, 2018
Weiler, 2018, Learning steerable filters for rotation equivariant CNNs
Wormanns, 2005, Detection of pulmonary nodules at multirow-detector ct: effectiveness of double reading to improve sensitivity at standard-dose and low-dose chest ct, Eur. J. Radiol., 15, 14, 10.1007/s00330-004-2527-6
Worrall, 2017, Harmonic networks: deep translation and rotation equivariance
Zhao, 2012, Performance of computer-aided detection of pulmonary nodules in low-dose ct: comparison with double reading by nodule volume., Eur. Radiol., 22, 10.1007/s00330-012-2437-y