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A first study exploring the performance of the state-of-the art CNN model in the problem of breast cancer

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Published:02 May 2018Publication History

ABSTRACT

During last decades, computer-assisted diagnosis systems for medical purposes have been highly developed. However, further research is still needed, especially for the diagnosis of very dangerous diseases such as breast cancer. For diagnosis, deep learning and more precisely Convolutional Neural Networks (CNNs) have shown a high potential in providing an automatic assistance to domain experts. This work explores and analyzes the performance of the state-of-the art CNN model in the problem of breast cancer histopathological images diagnosis.

References

  1. J. Ferlay. 2013. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012, Eur. J. Cancer 49, (2013), 1374--1403.Google ScholarGoogle ScholarCross RefCross Ref
  2. G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A.W.M. van der Laak, B. V. Ginneken and C. I. Sanchez. 2017. A Survey on Deep Learning in Medical Image Analysis, Medical Image Analysis 42, (2017), 60--88.Google ScholarGoogle ScholarCross RefCross Ref
  3. R. S. Cotran, V. Kumar, T. Collins, and S. L. Robbins. 1999. Robbins pathologic basis of disease (5th ed). Philadelphia: Saunders. (1999).Google ScholarGoogle Scholar
  4. F. Ghaznavi, A. Evans, A. Madabhushi, and M. Feldman. Digital imaging in pathology: whole-slide imaging and beyond. 2013. Annual Review of Pathology: Mechanisms of Disease 8, (2013), 331--359.Google ScholarGoogle ScholarCross RefCross Ref
  5. M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener. Histopathological image analysis: a review. 2009. IEEE Reviews in Biomedical Engineering 2, (2009), 147--171.Google ScholarGoogle ScholarCross RefCross Ref
  6. Aswathy, M.A. 2016. Detection of breast cancer on digital histopathology images: Present status and future possibilities. Informatics in Medicine Unlocked 8, (2016), 74--79.Google ScholarGoogle Scholar
  7. H. Mohan Textbook of Pathology (7th ed). New Delhi, India: Jaypee Brothers Medical Publishers Ltd. (2014).Google ScholarGoogle Scholar
  8. American Cancer Society: Testing biopsy and cytology specimens for cancer. https://www.cancer.org/treatment/understanding-your-diagnosis/tests/testing-biopsy-and-cytology-specimens-for-cancer.htmlGoogle ScholarGoogle Scholar
  9. F. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte. 2016. A dataset for breast cancer histopathological image classification. IEEE Transactions of Biomedical Engineering 63, (2016), 1455 - 1462.Google ScholarGoogle ScholarCross RefCross Ref
  10. Y. Bengio, A. Courville, and P. Vincent. 2013. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, (2013), 1798- 1828. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Y. LeCun, Y. Bengio, and G. Hinton. 2015. Deep learning Nature 521, (2015). 436 - 444.Google ScholarGoogle Scholar
  12. J. Deng, W. Dong, R. Socher, L-J. Li, K. Li, and L. Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009). IEEE, Miami, FL, USA, 248--255.Google ScholarGoogle Scholar
  13. F. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte. 2016. Breast cancer histopathological image classification using convolutional neural networks. in Proceedings of International Joint Conference on Neural Networks (IJCNN 2016). IEEE, Vancouver, BC, Canada, 2560--2567.Google ScholarGoogle Scholar
  14. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, (1998), 2278--2324.Google ScholarGoogle ScholarCross RefCross Ref
  15. A. Krizhevsky, I. Sutskever, and G. E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Proceedings of 26th Annual Conference on Neural Information Processing Systems (NIPS 2012). ACM, Lake Tahoe, Nevada, 1097--1105. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. F. Spanhol, P. Cavalin, L. S. Oliveira, C. Petitjean and L. Heutte. 2017. Deep Features for Breast Cancer Histopathological Image Classification. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics (IEEE SMC 2017). IEEE, Banff, Canada.Google ScholarGoogle Scholar
  17. J. Donahue, J. Yangqing, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng and T. Darrell. 2014. Decaf: A deep convolutional activation feature for generic visual recognition. In Proceedings of the 31th International Conference on Machine Learning (ICML 2014). ACM, Beijing, China, 647- 655. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich. 2015. Going deeper with convolutions. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015). IEEE, Boston, MA, USA.Google ScholarGoogle Scholar
  19. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition (CVPR 2016). IEEE, Las Vegas, NV, USA, 2818--2826.Google ScholarGoogle Scholar
  20. L. Bottou. 2012. Stochastic Gradient Descent Tricks. In Neural Networks: Tricks of the Trade (2nd ed). Lecture Notes in Computer Science, Vol. 7700. Springer-Heidelberg, 430--445.Google ScholarGoogle Scholar
  21. M. Abadi et al. 2016. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXivPrepr. (2016), arXiv:1603.04467.Google ScholarGoogle Scholar
  22. S. Tabik, D. Peralta, A. Herrera-Poyatos, F. Herrera. 2017. A snapshot of image pre-processing for convolutional neural networks: Case study of MNIST, International Journal of Computational Intelligence Systems, 10, (2017), 555--568.Google ScholarGoogle Scholar
  23. R. Olmos, S. Tabik, F. Herrera. Automatic handgun detection alarm in videos using deep learning. Neurocomputing, 275, 66--72. (2018) Google ScholarGoogle ScholarCross RefCross Ref
  24. E. Guirado, S. Tabik, D. Alcaraz-Segura, J. Cabello, F. Herrera. Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus Lotus as Case Study. Remote Sensing, 9(12), 1220. (2017)Google ScholarGoogle Scholar

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      • Published in

        cover image ACM Other conferences
        LOPAL '18: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications
        May 2018
        357 pages
        ISBN:9781450353045
        DOI:10.1145/3230905

        Copyright © 2018 ACM

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        Publication History

        • Published: 2 May 2018

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        LOPAL '18 Paper Acceptance Rate61of141submissions,43%Overall Acceptance Rate61of141submissions,43%

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