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Mammography is the most widely used method to screen breast cancer.
The digital database for screening mammography
K. Bowyer, D. Kopans, W. Kegelmeyer, R. Moore, M. Sallam, K. Chang, and K. Woods · 1996
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Digital mammographic computer aided diagnosis (cad) using adaptive level set segmentation
J. E. Ball and L. M. Bruce · 2007
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World cancer report 2008
P. Boyle, B. Levin, et al · 2008
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Variability in interpretive performance at screening mammography and radiologists’ characteristics associated with accuracy 1
J. G. Elmore, S. L. Jackson, L. Abraham, D. L. Miglioretti, P. A. Carney, B. M. Geller, B. C. Yankaskas, K. Kerlikowske, T. Onega, R. D. Rosenberg, et al · 2009
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Breast cancer statistics, 2013
C. DeSantis, J. Ma, L. Bryan, and A. Jemal · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
Cited alongside, same era.
Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
Cited alongside, same era.
Chest pathology detection using deep learning with non-medical training
Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, and H. Greenspan · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans
B. van Ginneken, A. A. Setio, C. Jacobs, and F. Ciompi · 2015
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Representation learning for mammography mass lesion classification with convolutional neural networks
J. Arevalo, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. A. G. Lopez · 2016
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Unregistered multiview mammogram analysis with pre-trained deep learning models
G. Carneiro, J. Nascimento, and A. P. Bradley · 2015
Cited alongside, same era.
Automated mass detection in mammograms using cascaded deep learning and random forests
N. Dhungel, G. Carneiro, and A. P. Bradley · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Deep learning and structured prediction for the segmentation of mass in mammograms
N. Dhungel, G. Carneiro, and A. P. Bradley
Cited in the paper.
Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning
H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers · 2016
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Discrimination of breast cancer with microcalcifications on mammography by deep learning
J. Wang, X. Yang, H. Cai, W. Tan, C. Jin, and L. Li · 2016
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