Abstract
Automatic detection of breast mass from mammograms is a challenging task. Recently, Convolution Neural Networks (CNNs) have been proposed to address this task. However, the performance of these CNN-based detection methods is still limited due to high complexity of breast tissue and varying shape of masses. An Automatic Mass Detection framework with Region-based CNN (AMDR-CNN) is presented in this paper, aiming to efficiently exploit informative features from mammograms. Under a hierarchical candidate mass region generation method with a full-size mammogram, the mammogram is greatly simplified and high-quality region proposals are generated. Then, a deeper CNN is introduced, which simultaneously predicts object bounds and scores at each position. In contrast to previous works, the deeper CNN learns the effective features of mass as well as helps produce accurate detection results. The experiments are performed on two public datasets, which achieves a better performance than state-of-the-art algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ferlay, J., Héry, C., Autier, P., Sankaranarayanan, R.: Global burden of breast cancer. In: Li, C. (ed.) Breast Cancer Epidemiology, pp. 1–19. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-0685-4_1
Broeders, M., et al.: The impact of mammography screening on breast cancer mortality in Europe: a review of observational studies. J. Med. Screen. 19(suppl 1), 14–25 (2012)
Tabar, L., et al.: Mammography service screening and mortality in breast cancer patients: 20-year follow-up before and after introduction of screening. Lancet 361(9367), 1405–1410 (2003)
Elmore, J.G., et al.: Variability in interpretive performance at screening mammography and radiologists’ characteristics associated with accuracy1. Breast Dis. Year Book Q. 21(4), 330–332 (2010)
Barlow, W.E., et al.: Accuracy of screening mammography interpretation by characteristics of radiologists. J. Natl. Cancer Inst. 97(12), 1840–1850 (2005)
Brewer, N., Salz, T., Lillie, S.E.: Systematic review: the long-term effects of false-positive mammograms. Ann. Intern. Med. 147(10), 739–740 (2007)
Myers, E.R., et al.: Benefits and harms of breast cancer screening: a systematic review. JAMA 314(15), 1615 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems (2012)
He, K., et al.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification, pp. 1026–1034 (2015)
Kooi, T., et al.: Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35, 303–312 (2017)
Becker, A.S., et al.: Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Investig. Radiol. 52(7), 434 (2017)
Dhungel, N., Carneiro, G., Bradley, A.P.: Fully automated classification of mammograms using deep residual neural networks. In: IEEE International Symposium on Biomedical Imaging (2017)
Lotter, W., Sorensen, G., Cox, D.: A multi-scale CNN and curriculum learning strategy for mammogram classification. In: Cardoso, M.J. (ed.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 169–177. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_20
Moreira, I.C., et al.: INbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012)
Ertosun, M.G., Rubin, D.L.: Probabilistic visual search for masses within mammography images using deep learning. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 1310–1315. IEEE (2015)
Arevalo, J., et al.: Representation learning for mammography mass lesion classification with convolutional neural networks. Comput. Methods Programs Biomed. 127(C), 248–257 (2016)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2014)
Szegedy, C., et al.: Inception-v4, Inception-ResNet and the impact of residual connections on learning (2016)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: International Conference on Neural Information Processing Systems (2015)
Girshick, R.: Fast R-CNN. In: IEEE International Conference on Computer Vision (2015)
Kozegar, E., et al.: Assessment of a novel mass detection algorithm in mammograms. J. Cancer Res. Ther. 9(4), 592 (2013)
Eltonsy, N.H., Tourassi, G.D., Elmaghraby, A.S.: A concentric morphology model for the detection of masses in mammography. IEEE Trans. Med. Imag. 26(6), 880 (2007)
Sampat, M.P., et al.: A model-based framework for the detection of spiculated masses on mammography. Med. Phys. 35(5), 2110–2123 (2008)
Dhungel, N., Carneiro, G., Bradley, A.P.: Automated mass detection in mammograms using cascaded deep learning and random forests. In: International Conference on Digital Image Computing: Techniques and Applications (2016)
Campanini, R., Dongiovanni, D., Iampieri, E., et al.: A novel featureless approach to mass detection in digital mammograms based on support vector machines. Phys. Med. Biol. 49(6), 961–975 (2004)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wu, Y., Shi, W., Cui, L., Wang, H., Bu, Q., Feng, J. (2018). Automatic Mass Detection from Mammograms with Region-Based Convolutional Neural Network. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_44
Download citation
DOI: https://doi.org/10.1007/978-981-13-1702-6_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1701-9
Online ISBN: 978-981-13-1702-6
eBook Packages: Computer ScienceComputer Science (R0)