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Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone.
doi:10.1162/neco.1989.1.4.541
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, Backpropagation applied to handwritten zip code recognition, Neural Computation 1 (4) (1989) 541–551 · 1989
Earlier work this paper cites.
C. W Elston, I. Ellis, Pathological prognostic factors in breast cancer. i. the value of histological grade in breast cancer: experience from a large study with long-term follow-up. c. w. elston & i. o. ellis. histopathology 1991; 19; 403-410. author commentary 41 (2002) 151–2, discussion 152
2002
Earlier work this paper cites.
doi:10.1109/ISBI.2009.5193250
M. Macenko, M. Niethammer, J. Marron, D. Borland, J. Woosley, X. Guan, C. Schmitt, N. Thomas, A method for normalizing histology slides for quantitative analysis, in: Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, 2009, pp. 1107–1110 · 2009
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks , in: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, NIPS’12, Curran Associates Inc., USA, 2012, pp. 1097–1105. URL http://dl.acm.org/citation.cfm?id=2999134.2999257
2012
Earlier work this paper cites.
L. Roux, D. Racoceanu, N. Lomenie, M. Kulikova, H. Irshad, J. Klossa, F. Capron, C. Genestie, G. Le Naour, M. N Gurcan, Mitosis detection in breast cancer histological images an icpr 2012 contest 4 (2013) 8
2013
Earlier work this paper cites.
D. C. Cireşan, A. Giusti, L. M. Gambardella, J. Schmidhuber, Mitosis detection in breast cancer histology images with deep neural networks, in: K. Mori, I. Sakuma, Y. Sato, C. Barillot, N. Navab (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, pp. 411–418
2013
Cited alongside, same era.
doi:10.1016/j.media.2014.11.010
M. Veta, P. van Diest, S. Willems, H. Wang, A. Madabhushi, A. Cruz-Roa, F. Gonzalez, A. Larsen, J. Vestergaard, A. Dahl, D. Cireşan, J. Schmidhuber, A. Giusti, L. Gambardella, F. Tek, T. Walter, C. Wang, S. Kondo, B. Matuszewski, F. Precioso, V. Snell, J. Kittler, T. de Campos, A. Khan, N. Rajpoot, E. Arkoumani, M. Lacle, M. Viergever, J. Pluim, Assessment of algorithms for mitosis detection in breast cancer histopathology images, Medical Image Analysis 20 (1) (2015) 237–248 · 2014
Cited alongside, same era.
S. Ren, K. He, R. Girshick, J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks , in: C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28, Curran Associates, Inc., 2015, pp. 91–99. URL http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf
2015
Cited alongside, same era.
H. Chen, Q. Dou, X. Wang, J. Qin, P. Heng, Mitosis detection in breast cancer histology images via deep cascaded networks (2016). URL https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11788/11717
2016
Later among the works it cites.
doi:10.1109/TMI.2016.2528120
S. Albarqouni, C. Baur, F. Achilles, V. Belagiannis, S. Demirci, N. Navab, Aggnet: Deep learning from crowds for mitosis detection in breast cancer histology images, IEEE Transactions on Medical Imaging 35 (5) (2016) 1313–1321 · 2016
Later among the works it cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, Tensorflow: A system for large-scale machine learning , in: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), 2016, pp. 265–283. URL https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf
2016
Later among the works it cites.
doi:10.1109/ICME.2017.8019550
C. Eggert, S. Brehm, A. Winschel, D. Zecha, R. Lienhart, A closer look: Small object detection in faster r-cnn , in: 2017 IEEE International Conference on Multimedia and Expo (ICME), Vol. 00, 2017, pp. 421–426 · 2017
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M. Ghoncheh, Z. Pournamdar, H. Salehiniya, Incidence and mortality and epidemiology of breast cancer in the world 17 (2016) 43–46
2016
Cited alongside, same era.
L. Roux, D. Racoceanu, Mitos & atypia detection of mitosis and evaluation of nuclear atypia score in breast cancer histological images
Cited in the paper.
Cited in the paper.
R. B. Girshick, Fast R-CNN , CoRR abs/1504.08083
Cited in the paper.
M. Saha, C. Chakraborty, D. Racoceanu, Efficient deep learning model for mitosis detection using breast histopathology images 64
Cited in the paper.
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition
Cited in the paper.
Later among the works it cites.