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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
DOI:10.26044/ecr2019/C-2062

References

[1] A. C. Society, Global Cancer Facts & Figures. Atlanta: American Cancer Society, third ed., 2015.

 

[2] B. Sahiner, H.-P. Chan, N. Petrick, D. Wei, M. A. Helvie, D. D. Adler, and M. M. Goodsitt, “Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images,” IEEE Transactions on Medical Imaging, vol. 15, pp. 598–610, Oct 1996.

 

[3] P. Fonseca, J. Mendoza, J. Wainer, J. Ferrer, J. Pinto, J. Guerrero, and B. Castaneda, “Automatic breast density classification using a convolutional neural network architecture search procedure,” in Proc. of SPIE Vol, vol. 9114, pp. 911128–1, 2015.

 

[4] 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. 04501–04501, 2016.

 

[5] N. Dhungel, G. Carneiro, and A. P. Bradley, The Automated Learning of Deep Features for Breast Mass Classification from Mammograms, pp. 106–114. Cham: Springer International Publishing, 2016.

 

[6] M. U. Dalmı¸ s, G. Litjens, K. Holland, A. Setio, R. Mann, N. Karssemeijer, and A. Gubern-Mérida, “Using deep learning to segment breast and fibroglandular tissue in mri volumes,” Medical Physics, vol. 44, no. 2, pp. 53–546, 2017.

 

[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.

 

[12] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” CoRR, vol. abs/1512.00567, 2015.

 

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