Abstract
Breast cancer is one of the leading cause of cancer-related death worldwide. During the diagnosis of breast cancer, the histopathological assessment of Haemotoxylin and Eosin(H&E) stained slides provides important clinical values. By applying computer-aid diagnosis on whole-slide image(WSI), the efficiency and consistency of such assessment could be improved. In this paper, we propose a deep learning-based framework that classifies H&E stained WSIs into regions of normal tissue, benign lesion, in-situ carcinoma and invasive carcinoma. The framework utilizes both microscopy images and WSIs to train a patch classifier in two stages. The underlying classifier is based on Inception-Resnet-v2. This framework won both parts of the ICIAR2018 Grand Challenge on Breast Cancer Histology Images [4] competition, achieved a part A multiclass accuracy of 87% and part B score of 0.6929.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Camelyon16 (2016). https://camelyon16.grand-challenge.org/results/
Camelyon17 (2017). https://camelyon17.grand-challenge.org/results/
Breast Cancer Facts and Figures 2017–2018 (2018). https://www.cancer.org/research/cancer-facts-statistics/breast-cancer-facts-figures.html
ICIAR 2018 Grand Challenge on Breast Cancer Histology Images (2018). https://iciar2018-challenge.grand-challenge.org/
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), 1–14 (2017). https://doi.org/10.1371/journal.pone.0177544
Elmore, J.G., Longton, G.M., Carney, P.A., Geller, B.M., Onega, T., Tosteson, A.N.A., et al.: Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313(11), 1122–1132 (2015). https://doi.org/10.1001/jama.2015.1405
Habibzadeh, M.N., Jannesary, M., Aboulkheyr, H., Khosravi, P., Elemento, O., Totonchi, M., Hajirasouliha, I.: Breast cancer histopathological image classification: a deep learning approach. bioRxiv (2018). https://www.biorxiv.org/content/early/2018/01/04/242818
Jain, R.K., Mehta, R., Dimitrov, R., Larsson, L.G., Musto, P.M., Hodges, K.B., Ulbright, T.M., Hattab, E.M., Agaram, N., Idrees, M.T., Badve, S.: Atypical ductal hyperplasia: interobserver and intraobserver variability. Mod. Pathol. 24, 917–923 (2011)
Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform. 7(1), 29 (2016). http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2016;volume=7;issue=1;spage=29;epage=29;aulast=Janowczyk;t=6
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Schnitt, S., Connolly, J., Tavassoli, F.A., Fechner, R., Kempson, R.L., Gelman, R., Page, D.: Interobserver reproducibility in the diagnosis of ductal proliferative breast lesions using standardized criteria. Am. J. Surg. Pathol. 16(12), 1133–1143 (1992)
Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. ArXiv e-prints, September 2014
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2016)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, ArXiv e-prints, February 2016
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going Deeper with Convolutions. ArXiv e-prints, September 2014
Zhong, A., Li, Q.: HMS-MGH-CCDS Camelyon17 presentation (2017). https://camelyon17.grand-challenge.org/serve/public_html/presentations/HMS-MGH-CCDS_Camelyon17_presentation.pptx
Acknowledgements
We would like to thank the organizers of ICIAR2018 and BACH2018 who supported and organized this challenge.
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
Kwok, S. (2018). Multiclass Classification of Breast Cancer in Whole-Slide Images. 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_106
Download citation
DOI: https://doi.org/10.1007/978-3-319-93000-8_106
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)