Abstract
Millions of women succumb to breast cancer every year. Till date, it is mainly diagnosed by core needle biopsy of the breast tissue, followed by analysis of the histopathological image to detect the presence of malignant tumor. In the past few years, deep learning pipelines have been proposed for carcinoma type classification from the breast histology images. They mostly entail in dividing the high-resolution images into patches, followed by classifying the patches using convolutional neural network and finally integrating the patch-wise results for predicting the class of the image. But these methods give the same importance to all the patches and do not focus on the most salient regions of the image. In this paper, we present a novel attention mechanism, which aids the network to specifically focus on the most relevant parts of the image, that is, the design of the network allows for learning a weighted representation of all the constituent patches of an image. Experimental results reveal that our model achieved a \(85.50\%\) and \(96.25\%\) for patch- and image-wise classification accuracies, respectively, on the ICIAR 2018 breast histopathological images dataset. Our proposed method outperforms some state-of-the-art methods to the best of our knowledge.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
T. Araújo, G. Aresta, E. Castro, J. Rouco, P. Aguiar, C. Eloy, A. Polónia, A. Campilho, Classification of breast cancer histology images using convolutional neural networks. PloS one 12(6), e0177544 (2017)
G. Aresta, T. Araújo, S. Kwok, S.S. Chennamsetty, M. Safwan, V. Alex, B. Marami, M. Prastawa, M. Chan, M. Donovan et al., Bach: Grand challenge on breast cancer histology images. Med. Image Anal. (2019)
A. Golatkar, D. Anand, A. Sethi, Classification of breast cancer histology using deep learning, in International Conference Image Analysis and Recognition (Springer, Berlin, 2018), pp. 837–844
D.P. Kingma, J. Ba, Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
M. Lin, Q. Chen, S. Yan, Network in network. arXiv preprint arXiv:1312.4400 (2013)
K. Nazeri, A. Aminpour, M. Ebrahimi, Two-stage convolutional neural network for breast cancer histology image classification, in International Conference Image Analysis and Recognition (Springer, Berlin, 2018), pp. 717–726
A. Rakhlin, A. Shvets, V. Iglovikov, A.A. Kalinin, Deep convolutional neural networks for breast cancer histology image analysis, in International Conference Image Analysis and Recognition (Springer, Berlin, 2018), pp. 737–744
K. Roy, D. Banik, D. Bhattacharjee, M. Nasipuri, Patch-based system for classification of breast histology images using deep learning. Comput. Med. Imaging Graphics 71, 90–103 (2019)
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
A. Vahadane, T. Peng, A. Sethi, S. Albarqouni, L. Wang, M. Baust, K. Steiger, A.M. Schlitter, I. Esposito, N. Navab, Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35(8), 1962–1971 (2016)
Y.S. Vang, Z. Chen, X. Xie, Deep learning framework for multi-class breast cancer histology image classification, in International Conference Image Analysis and Recognition (Springer, Berlin, 2018), pp. 914–922
R. Yan, F. Ren, Z. Wang, L. Wang, T. Zhang, Y. Liu, X. Rao, C. Zheng, F. Zhang, Breast cancer histopathological image classification using a hybrid deep neural network. Methods (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sanyal, R., Jethanandani, M., Sarkar, R. (2021). DAN : Breast Cancer Classification from High-Resolution Histology Images Using Deep Attention Network. In: Sharma, M.K., Dhaka, V.S., Perumal, T., Dey, N., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision. Advances in Intelligent Systems and Computing, vol 1189. Springer, Singapore. https://doi.org/10.1007/978-981-15-6067-5_35
Download citation
DOI: https://doi.org/10.1007/978-981-15-6067-5_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6066-8
Online ISBN: 978-981-15-6067-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)