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ECR 2019 / C-2062
Representation learning approach to breast cancer diagnosis
Congress: ECR 2019
Poster No.: C-2062
Type: Scientific Exhibit
Keywords: Breast, Artificial Intelligence, Computer applications, Mammography, Computer Applications-Detection, diagnosis, Diagnostic procedure, Decision analysis, Cancer, Image verification
Authors: J. P. Pereira Fontes, M. A. Guevara Lopez; Guimarães/PT


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[7] S. V. Fotin, Y. Yin, H. Haldankar, J. W. Hoffmeister, and S. Periaswamy, “Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches,” in Proc. SPIE, vol. 9785, 2016.


[8] 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. 5, pp. 303 – 312, 2017.


[9] J. Arevalo, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. A. Guevara Lopez, “Representation learning for mammography mass lesion classification with convolutional neural networks,” Computer methods and programs in biomedicine, vol. 127, pp. 248–257, 2016.


[10] D. C. Moura and M. A. Guevara Lopez, “An evaluation of image descriptors combined with clinical data for breast cancer diagnosis,” International Journal of Computer Assisted Radiology and Surgery, vol. 8, pp. 561–574, Jul 2013.


[11] M. A. Guevara Lopez, N. Posada, D. C. Moura, R. R. Pollán, J. M. F. Valiente, C. S. Ortega, M. Solar, G. Diaz-Herrero, I. Ramos, J. Loureiro, et al., “BCDR: a breast cancer digital repository,” in 15th International Conference on Experimental Mechanics, 2012.


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