Pulmonary nodule detection in CT scans with equivariant CNNs

Medical Image Analysis - Tập 55 - Trang 15-26 - 2019
Marysia Winkels1,2, Taco S. Cohen1
1University of Amsterdam, Netherlands
2Aidence B.V., Netherlands

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