Conditional Generative Adversarial Networks for Data Augmentation in Breast Cancer Classification

  • Weng San WongEmail author
  • Mohammed Amer
  • Tomas Maul
  • Iman Yi Liao
  • Amr Ahmed
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 978)


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.


Breast cancer classification Deep learning Histopathological images Data augmentation CGANs 


  1. 1.
  2. 2.
  3. 3.
    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.
  4. 4.
    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.
  5. 5.
    Chattoraj, S., Vishwakarma, K.: Classification of histopathological breast cancer images using iterative VMD aided zernike moments & textural signatures. CoRR abs/1801.04880.
  6. 6.
    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.
  7. 7.
    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.
  8. 8.
    Gadelha M, Maji S, Wang R (2016) 3d shape induction from 2d views of multiple objects. CoRR abs/1612.05872.
  9. 9.
    Gauthier J (2015) Conditional generative adversarial nets for convolutional face generationGoogle Scholar
  10. 10.
    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.
  11. 11.
    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) workshopsGoogle Scholar
  12. 12.
    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.
  13. 13.
    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.
  14. 14.
    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 interactionGoogle Scholar
  15. 15.
    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 ImagingGoogle Scholar
  16. 16.
    Li Y, Liu S, Yang J, Yang M (2017) Generative face completion. CoRR abs/1704.05838.
  17. 17.
    Ma L, Jia X, Sun Q, Schiele B, Tuytelaars T, Gool LV (2017) Pose guided person image generation. CoRR abs/1705.09368.
  18. 18.
    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).
  19. 19.
    Nahid A, Kong Y (2018) Histopathological breast-image classification using local and frequency domains by convolutional neural network. Information 9:19.
  20. 20.
    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. Scholar
  21. 21.
    Nazeri K, Aminpour A, Ebrahimi M (2018) Two-stage convolutional neural network for breast cancer histology image classification. CoRR abs/1803.04054.
  22. 22.
    Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. CoRR abs/1712.04621.
  23. 23.
    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.
  24. 24.
    Spanhol FA, de Oliveira LES, Petitjean C, Heutte L (2016) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63:1455–1462CrossRefGoogle Scholar
  25. 25.
    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.
  26. 26.
    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.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Weng San Wong
    • 1
    Email author
  • Mohammed Amer
    • 1
  • Tomas Maul
    • 1
  • Iman Yi Liao
    • 1
  • Amr Ahmed
    • 1
  1. 1.School of Computer ScienceUniversity of Nottingham MalaysiaSemenyihMalaysia

Personalised recommendations