Abstract
Automatic breast cancer classification benefits pathologists in obtaining fast and precise diagnoses and improving early detection. However, the performance of deep learning models depends greatly on the quality and quantity of the datasets used. Due to the complexity and high costs of patient data collection, many medical datasets, particularly for pathological conditions, suffer from small sample sizes. Hence, developing a deep learning solution for breast cancer classification is still challenging. Data augmentation is one of the popular approaches to bridge this gap. In this work, we propose to use Conditional Generative Adversarial Networks (CGANs) for data augmentation. The aim of training CGANs is to generate a new set of realistic synthetic images and combine these together with real images to form a new augmented training set. The experiments show that most of the images produced by CGAN are reliable and classification performance with CGAN-based data augmentation can achieve good results. This method, unlike traditional data augmentation, can produce histopathological images that are completely different from the existing data. Therefore, this technique has the potential to address data scarcity and to directly benefit the training of deep learning models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cancer: Breast cancer. https://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/
Why is early diagnosis important? (2018). https://www.cancerresearchuk.org/about-cancer/cancer-symptoms/why-is-early-diagnosis-important
Bayramoglu N, Kannala J, Heikkil J (Dec 2016) Deep learning for magnification independent breast cancer histopathology image classification. In: 2016 23rd international conference on pattern recognition (ICPR), pp 2440–2445. https://doi.org/10.1109/ICPR.2016.7900002
Bowles C, Chen L, Guerrero R, Bentley P, Gunn R, Hammers A, Dickie DA, Valdés Hernández M, Wardlaw J, Rueckert D (2018) GAN augmentation: augmenting training data using generative adversarial networks. CoRR abs/1810.10863. http://arxiv.org/abs/1810.10863
Chattoraj, S., Vishwakarma, K.: Classification of histopathological breast cancer images using iterative VMD aided zernike moments & textural signatures. CoRR abs/1801.04880. http://arxiv.org/abs/1801.04880
Dimitropoulos K, Barmpoutis P, Zioga C, Kamas A, Patsiaoura K, Grammalidis N (2017) Grading of invasive breast carcinoma through grassmannian vlad encoding. PLOS One 12(9):1–18. https://doi.org/10.1371/journal.pone.0185110
Frid-Adar M, Klang E, Amitai M, Goldberger J, Greenspan H (2018) Synthetic data augmentation using GAN for improved liver lesion classification. CoRR abs/1801.02385. http://arxiv.org/abs/1801.02385
Gadelha M, Maji S, Wang R (2016) 3d shape induction from 2d views of multiple objects. CoRR abs/1612.05872. http://arxiv.org/abs/1612.05872
Gauthier J (2015) Conditional generative adversarial nets for convolutional face generation
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Proceedings of the 27th international conference on neural information processing systems, NIPS 2014, vol 2. MIT Press, Cambridge, MA, USA, pp 2672–2680. http://dl.acm.org/citation.cfm?id=2969033.2969125
Gupta V, Bhavsar A (June 2018) Sequential modeling of deep features for breast cancer histopathological image classification. In: The IEEE conference on computer vision and pattern recognition (CVPR) workshops
Habibzadeh Motlagh N, Jannesary M, Aboulkheyr H, Khosravi P, Elemento O, Totonchi M, Hajirasouliha I (2018) Breast cancer histopathological image classification: a deep learning approach. https://www.biorxiv.org/content/early/2018/01/04/242818
Jin Y, Zhang J, Li M, Tian Y, Zhu H, Fang Z (2017) Towards the automatic anime characters creation with generative adversarial networks. CoRR abs/1708.05509. http://arxiv.org/abs/1708.05509
Kårsnäs A (2014) Image analysis methods and tools for digital histopathology applications relevant to breast cancer diagnosis. PhD thesis, Uppsala University, Division of visual information and interaction, computerized image analysis and human-computer interaction
Kohli MD, Summers RM, Geis JR (2017) Medical image data and datasets in the era of machine learning whitepaper from the 2016 C-MIMI meeting dataset session. J Digit Imaging
Li Y, Liu S, Yang J, Yang M (2017) Generative face completion. CoRR abs/1704.05838. http://arxiv.org/abs/1704.05838
Ma L, Jia X, Sun Q, Schiele B, Tuytelaars T, Gool LV (2017) Pose guided person image generation. CoRR abs/1705.09368. http://arxiv.org/abs/1705.09368
Myung Jae L, Da Eun K, Dong Kun C, Hong L, Young Man K (2018) Deep convolution neural networks for medical image analysis. Int J Eng Technol 7(3.33). https://doi.org/10.14419/ijet.v7i3.33.18588
Nahid A, Kong Y (2018) Histopathological breast-image classification using local and frequency domains by convolutional neural network. Information 9:19. https://doi.org/10.3390/info9010019
Nawaz M, Sewissy AA, Soliman THA (2018) Multi-class breast cancer classification using deep learning convolutional neural network. Int J Adv Comput Sci Appl 9(6):316–332. https://doi.org/10.14569/IJACSA.2018.090645
Nazeri K, Aminpour A, Ebrahimi M (2018) Two-stage convolutional neural network for breast cancer histology image classification. CoRR abs/1803.04054. http://arxiv.org/abs/1803.04054
Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. CoRR abs/1712.04621. http://arxiv.org/abs/1712.04621
Spanhol FA, Oliveira LS, Petitjean C, Heutte L (July 2016) Breast cancer histopathological image classification using convolutional neural networks. In: 2016 international joint conference on neural networks (IJCNN), pp 2560–2567. https://doi.org/10.1109/IJCNN.2016.7727519
Spanhol FA, de Oliveira LES, Petitjean C, Heutte L (2016) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63:1455–1462
Wu J, Zhang C, Xue T, Freeman WT, Tenenbaum JB (2016) Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. CoRR abs/1610.07584. http://arxiv.org/abs/1610.07584
Yang L, Chou S, Yang Y (2017) Midinet: a convolutional generative adversarial network for symbolic-domain music generation using 1d and 2d conditions. CoRR abs/1703.10847. http://arxiv.org/abs/1703.10847
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wong, W.S., Amer, M., Maul, T., Liao, I.Y., Ahmed, A. (2020). Conditional Generative Adversarial Networks for Data Augmentation in Breast Cancer Classification. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-36056-6_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36055-9
Online ISBN: 978-3-030-36056-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)