Abstract
This paper explores the problem of breast tissue classification of microscopy images. Based on the predominant cancer type the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. Given a suitable training dataset, we utilize deep learning techniques to address the classification problem. Due to the large size of each image in the training dataset, we propose a patch-based technique which consists of two consecutive convolutional neural networks. The first “patch-wise” network acts as an auto-encoder that extracts the most salient features of image patches while the second “image-wise” network performs classification of the whole image. The first network is pre-trained and aimed at extracting local information while the second network obtains global information of an input image. We trained the networks using the ICIAR 2018 grand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method yields \(95\%\) accuracy on the validation set compared to previously reported \(77\%\) accuracy rates in the literature. Our code is publicly available at https://github.com/ImagingLab/ICIAR2018.
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
Notes
- 1.
Our code and pre-trained weights are available at https://github.com/ImagingLab/ICIAR2018.
References
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2016. CA: Cancer J. Clin. 66(1), 7–30 (2016)
Ghaznavi, F., Evans, A., Madabhushi, A., Feldman, M.: Digital imaging in pathology: whole-slide imaging and beyond. Annu. Rev. Pathol. Mech. Dis. 8, 331–359 (2013)
Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., Campilho, A.: Classification of breast cancer histology images using convolutional neural networks. PloS one 12(6), e0177544 (2017)
Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2424–2433 (2016)
Cruz-Roa, A., Basavanhally, A., González, F., Gilmore, H., Feldman, M., Ganesan, S., Shih, N., Tomaszewski, J., Madabhushi, A.: Automatic detection of invasive Ductal carcinoma in whole slide images with convolutional neural networks. In: SPIE medical imaging, International Society for Optics and Photonics, vol. 9041, pp. 904103–904103 (2014)
ICIAR 2018 grand challenge: In: 15th International Conference on Image Analysis, Recognition. https://iciar2018-challenge.grand-challenge.org/
Kowal, M., Filipczuk, P., Obuchowicz, A., Korbicz, J., Monczak, R.: Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput. Biol. Med. 43(10), 1563–1572 (2013)
George, Y.M., Zayed, H.H., Roushdy, M.I., Elbagoury, B.M.: Remote computer-aided breast cancer detection and diagnosis system based on cytological images. IEEE Syst. J. 8(3), 949–964 (2014)
Filipczuk, P., Fevens, T., Krzyzak, A., Monczak, R.: Computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. IEEE Trans. Med. Imaging 32(12), 2169–2178 (2013)
Brook, A., El-Yaniv, R., Isler, E., Kimmel, R., Meir, R., Peleg, D.: Breast cancer diagnosis from biopsy images using generic features and SVMs. IEEE Transactions on Information Technology in Biomedicine (2006)
Zhang, B.: Breast cancer diagnosis from biopsy images by serial fusion of random subspace ensembles. In: 4th International Conference on Biomedical Engineering and Informatics (BMEI), vol. 1, pp. 180–186 IEEE (2011)
Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_51
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the ICML, vol. 30 (2013)
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net (2014). arXiv preprint arXiv:1412.6806
Lin, M., Chen, Q., Yan, S.: Network in network (2013). arXiv preprint:1312.4400
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 4898–4906 (2016)
Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G.E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P.Q., Corrado, G.S., et al.: Detecting cancer metastases on Gigapixel pathology images (2017). arXiv preprint arXiv:1703.02442
Macenko, M., Niethammer, M., Marron, J., Borland, D., Woosley, J.T., Guan, X., Schmitt, C., Thomas, N.E.: A method for normalizing histology slides for quantitative analysis. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009, ISBI’09., pp. 1107–1110, IEEE (2009)
Kingma, D., Ba, J.: Adam: A method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980
Acknowledgments
This research was supported in part by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (DG) for ME. AA would like to acknowledge UOIT for a doctoral graduate international tuition scholarship (GITS). The authors gratefully acknowledge the support of NVIDIA Corporation for their donation of Titan XP GPU used in this research through its Academic Grant Program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Nazeri, K., Aminpour, A., Ebrahimi, M. (2018). Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_81
Download citation
DOI: https://doi.org/10.1007/978-3-319-93000-8_81
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92999-6
Online ISBN: 978-3-319-93000-8
eBook Packages: Computer ScienceComputer Science (R0)