Abstract
Breast cancer risk assessment is becoming increasingly important in clinical practice. It has been suggested that features that characterize mammographic texture are more predictive for breast cancer than breast density. Yet, strong correlation between both types of features is an issue in many studies. In this work we investigate a method to generate texture features and/or scores that are independent of breast density. The method is especially useful in settings where features are learned from the data itself. We evaluate our method on a case control set comprising 394 cancers, and 1182 healthy controls. We show that the learned density independent texture features are significantly associated with breast cancer risk. As such it may aid in exploring breast characteristics that are predictive of breast cancer irrespective of breast density. Furthermore it offers opportunities to enhance personalized breast cancer screening beyond breast density.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Häberle, L., Wagner, F., Fasching, P.A., Jud, S.M., Heusinger, K., Loehberg, C.R., Hein, A., Bayer, C.M., Hack, C.C., Lux, M.P., et al.: Characterizing mammographic images by using generic texture features. Breast Cancer Res. 14(2), R59 (2012)
Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato (1999)
Hansen, B.B., Klopfer, S.O.: Optimal full matching and related designs via network flows. J. Comput. Graph. Stat. 15, 609–627 (2012)
Kallenberg, M., Lilholm, M., Diao, P., Petersen, K., Holland, K., Karssemeijer, N., Igel, C., Nielsen, M.: Assessing breast cancer masking risk with automated texture analysis in full field digital mammography. In: Annual Meeting of the Radiological Society of North America (2015)
Kallenberg, M., Petersen, K., Lilholm, M., Jørgensen, D., Diao, P., Holland, K., Karssemeijer, N., Igel, C., Nielsen, M.: Automated texture scoring for assessing breast cancer masking risk in full field digital mammography. In: ECR (2015)
Manduca, A., Carston, M., Heine, J., Scott, C., Pankratz, V., Brandt, K., Sellers, T., Vachon, C., Cerhan, J.: Texture features from mammographic images and risk of breast cancer. Cancer Epidemiol. Biomarkers Prev. 18, 837–845 (2009). http://dx.doi.org/10.1158/1055-9965.EPI-08-0631
Masci, J., Meier, U., Cireşan, D., Schmidhuber, J.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: Honkela, T. (ed.) ICANN 2011, Part I. LNCS, vol. 6791, pp. 52–59. Springer, Heidelberg (2011)
Nielsen, M., Vachon, C.M., Scott, C.G., Chernoff, K., Karemore, G., Karssemeijer, N., Lillholm, M., Karsdal, M.A.: Mammographic texture resemblance generalizes as an independent risk factor for breast cancer. Breast Cancer Res. 16, R37 (2014). http://dx.doi.org/10.1186/bcr3641
Petersen, K., Chernoff, K., Nielsen, M., Ng, A.: Breast density scoring with multiscale denoising autoencoders. In: Proceedings Sparsity Techniques in Medical Imaging 2012, in conjunction with MICCAI 2012 (2012)
Ranzato, M., Poultney, C.S., Chopra, S., LeCun, Y.: Efficient learning of sparse representations with an energy-based model. In: Advances in Neural Information Processing Systems, pp. 1137–1144 (2007)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: International Conference on Machine Learning, pp. 1096–1103 (2008)
Zheng, Y., Keller, B.M., Ray, S., Wang, Y., Conant, E.F., Gee, J.C., Kontos, D.: Parenchymal texture analysis in digital mammography: a fully automated pipeline for breast cancer risk assessment. Med. Phys. 42(7), 4149–4160 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kallenberg, M., Nielsen, M., Holland, K., Karssemeijer, N., Igel, C., Lillholm, M. (2016). Learning Density Independent Texture Features. In: Tingberg, A., LÃ¥ng, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-41546-8_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41545-1
Online ISBN: 978-3-319-41546-8
eBook Packages: Computer ScienceComputer Science (R0)