Commenced in January 2007
Paper Count: 30855
MITOS-RCNN: Mitotic Figure Detection in Breast Cancer Histopathology Images Using Region Based Convolutional Neural Networks
Authors: Siddhant Rao
Abstract: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. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming. It usually requires a trained pathologist to manually examine histopathological images and identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the three factors that characterize tumor belligerence described by the Nottingham Grading System: mitotic count. Other computational approaches to mitotic figure counting and detection do not demonstrate ample recall or precision to be clinically viable. Our models outperformed all previous participants in the ICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challenge along with recently published works. Our model achieved an F- measure score of 0.955, a 6.11% improvement in accuracy from the most accurate of the previously proposed models.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1475004Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 718
 M. Ghoncheh, Z. Pournamdar, and H. Salehiniya, “Incidence and mortality and epidemiology of breast cancer in the world,” vol. 17, pp. 43–46, 06 2016.
 C. W Elston and 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,” vol. 41, pp. 151–2, discussion 152, 10 2002.
 L. Roux and D. Racoceanu, “Mitos & atypia detection of mitosis and evaluation of nuclear atypia score in breast cancer histological images,” 06 2014.
 S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in Neural Information Processing Systems 28, C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, Eds. Curran Associates, Inc., 2015, pp. 91–99. (Online). Available: http://papers.nips.cc/paper/ 5638-faster-r-cnn-towards-real-time-object-detection-with-region-\ proposal-networks.pdf.
 Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural Computation, vol. 1, no. 4, pp. 541–551, Dec 1989.
 A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, ser. NIPS’12. USA: Curran Associates Inc., 2012, pp. 1097–1105. (Online). Available: http://dl.acm.org/citation.cfm?id= 2999134.2999257.
 R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” ArXiv e-prints, nov 2013.
 R. B. Girshick, “Fast R-CNN,” CoRR, vol. abs/1504.08083, 2015. (Online). Available: http://arxiv.org/abs/1504.08083.
 L. Roux, D. Racoceanu, N. Lomenie, M. Kulikova, H. Irshad, J. Klossa, F. Capron, C. Genestie, G. Le Naour, and M. N Gurcan, “Mitosis detection in breast cancer histological images an icpr 2012 contest,” vol. 4, p. 8, 05 2013.
 M. Veta, P. van Diest, S. Willems, H. Wang, A. Madabhushi, A. Cruz-Roa, F. Gonzalez, A. Larsen, J. Vestergaard, A. Dahl, D. Cirean, 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, and J. Pluim, “Assessment of algorithms for mitosis detection in breast cancer histopathology images,” Medical Image Analysis, vol. 20, no. 1, pp. 237–248, 2 2015.
 D. C. Cires¸an, A. Giusti, L. M. Gambardella, and J. Schmidhuber, “Mitosis detection in breast cancer histology images with deep neural networks,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, K. Mori, I. Sakuma, Y. Sato, C. Barillot, and N. Navab, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 411–418.
 H. Chen, Q. Dou, X. Wang, J. Qin, and P. Heng, “Mitosis detection in breast cancer histology images via deep cascaded networks,” 2016. (Online). Available: https://www.aaai.org/ocs/index.php/AAAI/ AAAI16/paper/view/11788/11717.
 S. Albarqouni, C. Baur, F. Achilles, V. Belagiannis, S. Demirci, and N. Navab, “Aggnet: Deep learning from crowds for mitosis detection in breast cancer histology images,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1313–1321, May 2016.
 M. Saha, C. Chakraborty, and D. Racoceanu, “Efficient deep learning model for mitosis detection using breast histopathology images,” vol. 64, 12 2017.
 M. Macenko, M. Niethammer, J. Marron, D. Borland, J. Woosley, X. Guan, C. Schmitt, and 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.
 K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 09 2014.
 C. Eggert, S. Brehm, A. Winschel, D. Zecha, and R. Lienhart, “A closer look: Small object detection in faster r-cnn,” in 2017 IEEE International Conference on Multimedia and Expo (ICME), vol. 00, July 2017, pp. 421–426. (Online). Available: doi.ieeecomputersociety. org/10.1109/ICME.2017.8019550.
 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, and 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. (Online). Available: https://www.usenix.org/system/files/ conference/osdi16/osdi16-abadi.pdf.