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Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades.
Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification
Riccardi, A., Petkov, T.S., Ferri, G., Masotti, M., Campanini, R., 2011 · 1971
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An introduction to the bootstrap. volume 57
Efron, B., Tibshirani, R.J., 1994 · 1994
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How swarms build cognitive maps, in: The biology and technology of intelligent autonomous agents. Springer, pp. 439–450
Chialvo, D.R., Millonas, M.M., 1995 · 1995
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Ensemble methods in machine learning, in: Multiple Classifier Systems. Springer Science Heidelberg, pp. 1–15
Dietterich, T.G., 2000 · 2000
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CT screening for lung cancer: Frequency and significance of part-solid and nonsolid nodules
Henschke, C.I., Yankelevitz, D.F., Mirtcheva, R., McGuinness, G., McCauley, D., Miettinen, O.S., 2002 · 2002
Earlier work this paper cites.
Selective enhancement filters for lung nodules, intracranial aneurysms, and breast microcalcifications, in: International Congress Series, Elsevier. pp. 929–934
Li, Q., Arimura, H., Doi, K., 2004 · 2004
Earlier work this paper cites.
Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans
Kuhnigk, J.M., Dicken, V., Bornemann, L., Bakai, A., Wormanns, D., Krass, S., Peitgen, H.O., 2006 · 2006
Earlier work this paper cites.
Receiver operating characteristic analysis in medical imaging
International Commission on Radiation Units and Measurements, 2008 · 2008
Earlier work this paper cites.
Lung nodule detection in low-dose and thin-slice computed tomography
Retico, A., Delogu, P., Fantacci, M.E., Gori, I., Martinez, A.P., 2008 · 2008
Earlier work this paper cites.
Assessment of radiologist performance in the detection of lung nodules: dependence on the definition of “truth”
Armato, S.G., Roberts, R.Y., Kocherginsky, M., Aberle, D.R., Kazerooni, E.A., Macmahon, H., van Beek, E.J.R., Yankelevitz, D., McLennan, G., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., P.Caligiuri, Quint, L.E., Sundaram, B., Croft, B.Y., Clarke, L.P., 2009 · 2009
Earlier work this paper cites.
3-d object segmentation using ant colonies
Cerello, P., Cheran, S.C., Bagnasco, S., Bellotti, R., Bolanos, L., Catanzariti, E., De Nunzio, G., Fantacci, M.E., Fiorina, E., Gargano, G., et al., 2010 · 2009
Earlier work this paper cites.
A novel multithreshold method for nodule detection in lung CT
Golosio, B., Masala, G.L., Piccioli, A., Oliva, P., Carpinelli, M., Cataldo, R., Cerello, P., De Carlo, F., Falaschi, F., Fantacci, M.E., Gargano, G., Kasae, P., Torsello, M., 2009 · 2009
Earlier work this paper cites.
A large scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification
Murphy, K., van Ginneken, B., Schilham, A.M.R., de Hoop, B.J., Gietema, H.A., Prokop, M., 2009 · 2009
Earlier work this paper cites.
Pleural nodule identification in low-dose and thin-slice lung computed tomography
Retico, A., Fantacci, M.E., Gori, I., Kasae, P., Golosio, B., Piccioli, A., Cerello, P., De Nunzio, G., Tangaro, S., 2009 · 2009
Earlier work this paper cites.
Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection
van Rikxoort, E.M., de Hoop, B., Viergever, M.A., Prokop, M., van Ginneken, B., 2009 · 2009
Earlier work this paper cites.
Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study
van Ginneken, B., Armato, S.G., de Hoop, B., van de Vorst, S., Duindam, T., Niemeijer, M., Murphy, K., Schilham, A.M.R., Retico, A., Fantacci, M.E., Camarlinghi, N., Bagagli, F., Gori, I., Hara, T., Fujita, H., Gargano, G., Belloti, R., Carlo, F.D., Megna, R., Tangaro, S., Bolanos, L., Cerello, P., Cheran, S.C., Torres, E.L., Prokop, M., 2010 · 2010
Earlier work this paper cites.
A new computationally efficient CAD system for pulmonary nodule detection in CT imagery
Messay, T., Hardie, R.C., Rogers, S.K., 2010 · 2010
Earlier work this paper cites.
On combining computer-aided detection systems
Niemeijer, M., Loog, M., Abràmoff, M.D., Viergever, M.A., Prokop, M., van Ginneken, B., 2011 · 2010
Earlier work this paper cites.
Reduced lung-cancer mortality with low-dose computed tomographic screening
Aberle, D.R., Adams, A.M., Berg, C.D., Black, W.C., Clapp, J.D., Fagerstrom, R.M., Gareen, I.F., Gatsonis, C., Marcus, P.M., Sicks, J.D., 2011 · 2011
Earlier work this paper cites.
The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans
Armato, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., Kazerooni, E.A., MacMahon, H., Beek, E.J.R.V., Yankelevitz, D., Biancardi, A.M., Bland, P.H., Brown, M.S., Engelmann, R.M., Laderach, G.E., Max, D., Pais, R.C., Qing, D.P.Y., Roberts, R.Y., Smith, A.R., Starkey, A., Batrah, P., Caligiuri, P., Farooqi, A., Gladish, G.W., Jude, C.M., Munden, R.F., Petkovska, I., Quint, L.E., Schwartz, L.H., Sundaram, B., Dodd, L.E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A.V., Gupte, S., Sallamm, M., Heath, M.D., Kuhn, M.H., Dharaiya, E., Burns, R., Fryd, D.S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B.Y., 2011 · 2011
Earlier work this paper cites.
Combination of computer-aided detection algorithms for automatic lung nodule identification
Camarlinghi, N., Gori, I., Retico, A., Bellotti, R., Bosco, P., Cerello, P., Gargano, G., Torres, E.L., Megna, R., Peccarisi, M., Fantacci, M.E., 2011 · 2011
Cited alongside, same era.
A novel computer-aided lung nodule detection system for CT images
Tan, M., Deklerck, R., Jansen, B., Bister, M., Cornelis, J., 2011 · 2011
Cited alongside, same era.
Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., Bengio, Y., 2012 · 2012
Cited alongside, same era.
Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models
Cascio, D., Magro, R., Fauci, F., Iacomi, M., Raso, G., 2012 · 2012
Cited alongside, same era.
High performance lung nodule detection schemes in CT using local and global information
Guo, W., Li, Q., 2012 · 2012
Cited alongside, same era.
Data from LIDC-IDRI
Armato III, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., Kazerooni, E.A., MacMahon, H., van Beek, E.J., Yankelevitz, D., Biancardi, A.M., Bland, P.H., Brown, M.S., Engelmann, R.M., Laderach, G.E., Max, D., Pais, R.C., Qing, D.P., Roberts, R.Y., Smith, A.R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G.W., Jude, C.M., Munden, R.F., Petkovska, I., Quint, L.E., Schwartz, L.H., Sundaram, B., Dodd, L.E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A.V., Gupte, S., Sallam, M., Heath, M.D., Kuhn, M.H., Dharaiya, E., Burns, R., Fryd, D.S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B.Y., Clarke, L.P., 2015 · 2015
Later among the works it cites.
Lasagne: First release
Dieleman, S., Schlüter, J., Raffel, C., Olson, E., Sønderby, S.K., Nouri, D., Maturana, D., Thoma, M., Battenberg, E., Kelly, J., et al., 2015 · 2015
Later among the works it cites.
Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans, in: IEEE International Symposium on Biomedical Imaging, pp. 286–289
van Ginneken, B., Setio, A.A.A., Jacobs, C., Ciompi, F., 2015 · 2015
Later among the works it cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034
He, K., Zhang, X., Ren, S., Sun, J., 2015 · 2015
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R., 2012 · 2012
Cited alongside, same era.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T., Hinton, G., 2012 · 2012
Cited alongside, same era.
Automated pulmonary nodule detection system in computed tomography images: A hierarchical block classification approach
Choi, W.J., Choi, T.S., 2013 · 2013
Cited alongside, same era.
The cancer imaging archive (TCIA): Maintaining and operating a public information repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., Prior, F., 2013 · 2013
Cited alongside, same era.
Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images
Jacobs, C., van Rikxoort, E.M., Twellmann, T., Scholten, E.T., de Jong, P.A., Kuhnigk, J.M., Oudkerk, M., de Koning, H.J., Prokop, M., Schaefer-Prokop, C., van Ginneken, B., 2014 · 2013
Cited alongside, same era.
Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the fleischner society
Naidich, D.P., Bankier, A.A., MacMahon, H., Schaefer-Prokop, C.M., Pistolesi, M., Goo, J.M., Macchiarini, P., Crapo, J.D., Herold, C.J., Austin, J.H., Travis, W.D., 2013 · 2013
Cited alongside, same era.
Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013, Springer. pp. 246–253
Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., Nielsen, M., 2013 · 2013
Cited alongside, same era.
Later among the works it cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
Later among the works it cites.
Automatic detection of large pulmonary solid nodules in thoracic CT images
Setio, A.A.A., Jacobs, C., Gelderblom, J., van Ginneken, B., 2015 · 2015
Later among the works it cites.
Going deeper with convolutions , 1–9doi:
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015 · 2015
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Large scale validation of the M5L lung CAD on heterogeneous CT datasets
Torres, E.L., Fiorina, E., Pennazio, F., Peroni, C., Saletta, M., Camarlinghi, N., Fantacci, M.E., Cerello, P., 2015 · 2015
Later among the works it cites.
Show, attend and tell: Neural image caption generation with visual attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R.S., Bengio, Y., 2015 · 2015
Later among the works it cites.
Cancer facts and figures 2016
American Cancer Society, 2016 · 2016
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Pulmonary nodule detection using a cascaded SVM classifier, in: Medical Imaging, International Society for Optics and Photonics. pp. 978513–978513
Bergtholdt, M., Wiemker, R., Klinder, T., 2016 · 2016
Closest in time.
Multi-level contextual 3D CNNs for false positive reduction in pulmonary nodule detection
Dou, Q., Chen, H., Yu, L., Qin, J., Heng, P.A., 2016 · 2016
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Deep residual learning for image recognition , 770–778doi:
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
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Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database
Jacobs, C., van Rikxoort, E.M., Murphy, K., Prokop, M., Schaefer-Prokop, C.M., van Ginneken, B., 2016 · 2016
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Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks
Setio, A.A.A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S., Wille, M.W., Naqibullah, M., Sanchez, C., van Ginneken, B., 2016 · 2016
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A discriminative feature learning approach for deep face recognition, in: European Conference on Computer Vision, Springer. pp. 499–515
Wen, Y., Zhang, K., Li, Z., Qiao, Y., 2016 · 2016
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Zagoruyko, S., Komodakis, N., 2016 · 2016
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Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans
Li, Q., Sone, S., Doi, K., 2003 · 2051
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Understanding the difficulty of training deep feedforward neural networks., in: Aistats, pp. 249–256
Glorot, X., Bengio, Y., 2010 · 2059
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