Abstract
Determining the severity and potential aggressiveness of breast cancer is an important step in the determination of the treatment options for a patient. Mitosis activity is one of the main components in breast cancer severity grading. Currently, mitosis counting is a laborious, prone to processing errors, done manually by a pathologist.
This paper presents a novel approach for automatic mitosis detection, where promising candidates are selected from a superpixel segmentation of the image and classified using an ensemble classifier created from a selection from a pool of different color spaces, different features vector.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Elston, C., Ellis, I.: 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. Histopathology 19(5) 403–410 (1991)
Tashk, A., Helfroush, M.S., Danyali, H., Akbarzadeh, M.: An automatic mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classification. In: The 5th Conference on Information and Knowledge Technology, pp. 406–410, May 2013
Khan, A.M., El-Daly, H., Rajpoot, N.M.: A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), pp. 149–152, November 2012
Chen, H., Dou, Q., Wang, X., Qin, J., Heng, P.A.: Mitosis detection in breast cancer histology images via deep cascaded networks. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 1160–1166. AAAI Press (2016)
Wang, H., Cruz-Roa, A., Basavanhally, A., Gilmore, H., Shih, N., Feldman, M., Tomaszewski, J., Gonzalez, F., Madabhushi, A.: Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection. In: SPIE Medical Imaging, vol. 9041, pp. 90410B–90410B-10, March 2014
MICCAI Grand Challenge: Tumor proliferation assessment challenge (tupac16). http://tupac.tue-image.nl
Macenko, M., Niethammer, M., Marron, J.S., Borland, D., Woosley, J.T., Guan, X., Schmitt, C., Thomas, N.E.: A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1107–1110, June 2009
Ren, X., Malik, J.: Learning a classification model for segmentation. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 10–17, October 2003
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Chang, H., Loss, L.A., Parvin, B.: Nuclear segmentation in h & e sections via multi-reference graph cut. In: International Symposium Biomedical Imaging (2012)
Chapelle, O., Haffner, P., Vapnik, V.: Svms for histogram-based image classification (1999)
Mohabey, A., Ray, A.: Rough set theory based segmentation of color images. In: 19th International Conference of the North American, Fuzzy Information Processing Society, NAFIPS, pp. 338–342(2000)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Int. Res. 16(1), 321–357 (2002)
Bonab, H.R., Can, F.: A theoretical framework on the ideal number of classifiers for online ensembles in data streams. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM 2016, pp. 2053–2056. ACM, New York (2016)
Ali, K.M., Pazzani, M.J.: Error reduction through learning multiple descriptions. Mach. Learn. 24(3), 173–202 (1996)
Abdi, H.: The kendall rank correlation coefficient. In: Salkind, N.J. (ed.) Encyclopedia of Measurement and Statistics. Sage, Thousand Oaks (2007)
Duin, R., Tax, D.: Experiments with Classifier Combining Rules, pp. 16–29 (2000)
Acknowledgment
This research has been supported by the Platform for advanced prescriptive health operational system (PAPHOS) project funded by EIT Health.
The authors would like to thank the organizers of the TUPAC16 Tumor Proliferation Assessment Challenge 2016 as well as all parties involved in preparing and providing the data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Toro, C.A.O., Martín, C.G., Pedrero, A.G., Gonzalez, A.R., Menasalvas, E. (2017). Mitosis Detection in Breast Cancer Using Superpixels and Ensemble Classifiers. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., Pinto, T. (eds) 11th International Conference on Practical Applications of Computational Biology & Bioinformatics. PACBB 2017. Advances in Intelligent Systems and Computing, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-60816-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-60816-7_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60815-0
Online ISBN: 978-3-319-60816-7
eBook Packages: EngineeringEngineering (R0)