Fetching the paper…
Reading the bibliography…
Advances in deep learning for natural images have prompted a surge of interest in applying similar techniques to medical images.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error-propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1 . MIT Press, Cambridge, MA, 1986, vol. 1, no. 6088, pp. 318–362
1986
Earlier work this paper cites.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural computation , vol. 1, no. 4, pp. 541–551, 1989
1989
Earlier work this paper cites.
K. Bowyer, D. Kopans, W. Kegelmeyer, R. Moore, M. Sallam, K. Chang, and K. Woods, “The digital database for screening mammography,” in Third international workshop on digital mammography , vol. 58, 1996, p. 27
1996
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
M. Heath, K. Bowyer, D. Kopans, P. Kegelmeyer Jr, R. Moore, K. Chang, and S. Munishkumaran, “Current status of the digital database for screening mammography,” in Digital mammography . Springer, 1998, pp. 457–460
1998
Earlier work this paper cites.
L. Tabar, B. Vitak, H. H. Chen, M. F. Yen, S. W. Duffy, and R. A. Smith, “Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality,” Cancer , vol. 91, no. 9, pp. 1724–31, 2001. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/11335897
2001
Earlier work this paper cites.
S. W. Duffy, L. Tabar, H. H. Chen, M. Holmqvist, M. F. Yen, S. Abdsalah, B. Epstein, E. Frodis, E. Ljungberg, C. Hedborg-Melander, A. Sundbom, M. Tholin, M. Wiege, A. Akerlund, H. M. Wu, T. S. Tung, Y. H. Chiu, C. P. Chiu, C. C. Huang, R. A. Smith, M. Rosen, M. Stenbeck, and L. Holmberg, “The impact of organized mammography service screening on breast carcinoma mortality in seven swedish counties,” Cancer , vol. 95, no. 3, pp. 458–69, 2002
2002
Earlier work this paper cites.
D. B. Kopans, “Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality,” Cancer , vol. 94, no. 2, pp. 580–1; author reply 581–3, 2002. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/11900247
2002
Earlier work this paper cites.
S. W. Duffy, L. Tabar, and R. A. Smith, “The mammographic screening trials: commentary on the recent work by Olsen and Gotzsche,” CA Cancer J Clin , vol. 52, no. 2, pp. 68–71, 2002. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/11929006
2002
Earlier work this paper cites.
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks.” in International Conference on Artificial Intelligence and Statistics , 2010
2010
Earlier work this paper cites.
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, “Multimodal deep learning,” in International conference on machine learning , 2011, pp. 689–696
2011
Earlier work this paper cites.
J. S. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl, “Algorithms for hyper-parameter optimization,” in Advances in Neural Information Processing Systems 24 , 2011, pp. 2546–2554
2011
Earlier work this paper cites.
N. Srivastava and R. Salakhutdinov, “Multimodal learning with deep boltzmann machines,” in Advances in neural information processing systems , 2012, pp. 2222–2230
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
I. C. Moreira, I. Amaral, I. Domingues, A. Cardoso, M. J. Cardoso, and J. S. Cardoso, “Inbreast: toward a full-field digital mammographic database,” Academic radiology , vol. 19, no. 2, pp. 236–248, 2012
2012
Earlier work this paper cites.
M. Lin, Q. Chen, and S. Yan, “Network in network,” in International Conference on Learning Representations , 2013
2013
Cited alongside, same era.
I. Domingues and J. S. Cardoso, “Mass detection on mammogram images: a first assessment of deep learning techniques,” 2013
2013
Cited alongside, same era.
A. N. Tosteson, D. G. Fryback, C. S. Hammond, L. G. Hanna, M. R. Grove, M. Brown, Q. Wang, K. Lindfors, and E. D. Pisano, “Consequences of false-positive screening mammograms,” JAMA internal medicine , vol. 174, no. 6, pp. 954–961, 2014
2014
Cited alongside, same era.
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting.” Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Cited alongside, same era.
D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representation , 2015
2015
Later among the works it cites.
2015
Later among the works it cites.
J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M. M. A. Patwary, M. Prabhat, and R. P. Adams, “Scalable bayesian optimization using deep neural networks,” in International Conference on Machine Learning , 2015
2015
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition , 2016
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2015,” CA: a cancer journal for clinicians , vol. 65, no. 1, pp. 5–29, 2015
2015
Cited alongside, same era.
D. B. Kopans, “An open letter to panels that are deciding guidelines for breast cancer screening,” Breast Cancer Res Treat , vol. 151, no. 1, pp. 19–25, 2015. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/25868866
2015
Cited alongside, same era.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Cited alongside, same era.
W. Wang, R. Arora, K. Livescu, and J. Bilmes, “On deep multi-view representation learning,” in International Conference on Machine Learning , 2015, pp. 1083–1092
2015
Cited alongside, same era.
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller, “Multi-view convolutional neural networks for 3d shape recognition,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 945–953
2015
Cited alongside, same era.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representation , 2015
2015
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 1–9
2015
Cited alongside, same era.
B. Q. Huynh, H. Li, and M. L. Giger, “Digital mammographic tumor classification using transfer learning from deep convolutional neural networks,” Journal of Medical Imaging , vol. 3, no. 3, pp. 034 501–034 501, 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
J. Arevalo, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. A. G. Lopez, “Representation learning for mammography mass lesion classification with convolutional neural networks,” Computer methods and programs in biomedicine , vol. 127, pp. 248–257, 2016
2016
Later among the works it cites.
J.-J. Mordang, T. Janssen, A. Bria, T. Kooi, A. Gubern-Mérida, and N. Karssemeijer, “Automatic microcalcification detection in multi-vendor mammography using convolutional neural networks,” in International Workshop on Digital Mammography . Springer, 2016, pp. 35–42
2016
Later among the works it cites.
A. Akselrod-Ballin, L. Karlinsky, S. Alpert, S. Hasoul, R. Ben-Ari, and E. Barkan, “A region based convolutional network for tumor detection and classification in breast mammography,” in International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis . Springer, 2016, pp. 197–205
2016
Later among the works it cites.
2016
Later among the works it cites.
J. Wang, X. Yang, H. Cai, W. Tan, C. Jin, and L. Li, “Discrimination of breast cancer with microcalcifications on mammography by deep learning,” Scientific reports , vol. 6, p. 27327, 2016
2016
Later among the works it cites.
A. J. Bekker, H. Greenspan, and J. Goldberger, “A multi-view deep learning architecture for classification of breast microcalcifications,” in IEEE International Symposium on Biomedical Imaging , 2016, pp. 726–730
2016
Later among the works it cites.
T. Kooi, G. Litjens, B. van Ginneken, A. Gubern-Mérida, C. I. Sánchez, R. Mann, A. den Heeten, and N. Karssemeijer, “Large scale deep learning for computer aided detection of mammographic lesions,” Medical image analysis , vol. 35, pp. 303–312, 2017
2017
Closest in time.
A. S. Becker, M. Marcon, S. Ghafoor, M. C. Wurnig, T. Frauenfelder, and A. Boss, “Deep learning in mammography: Diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer.” Investigative Radiology , 2017
2017
Closest in time.