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Fusing of Deep Learning, Transfer Learning and GAN for Breast Cancer Histopathological Image Classification

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Book cover Advanced Computational Methods for Knowledge Engineering (ICCSAMA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1121))

Abstract

Biomedical image classification often deals with limited training sample due to the cost of labeling data. In this paper, we propose to combine deep learning, transfer learning and generative adversarial network to improve the classification performance. Fine-tuning on VGG16 and VGG19 network are used to extract the good discriminated cancer features from histopathological image before feeding into neuron network for classification. Experimental results show that the proposed approaches outperform the previous works in the state-of-the-art on breast cancer images dataset (BreaKHis).

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References

  1. Fotso Kamga Guy, A., Akram, T., Laurent, B., Naqvi, S.R., Alex, M.M., Muhammad, N.: A deep heterogeneous feature fusion approach for automatic land-use classification. Inf. Sci. 467, 199–218 (2018)

    Google Scholar 

  2. Alom, M.Z., Yakopcic, C., Nasrin, M.S., Taha, T.M., Asari, V.K.: Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. J. Digital Imaging, 1–15 (2019)

    Google Scholar 

  3. Badejo, J.A., Adetiba, E., Akinrinmade, A., Akanle, M.B.: Medical image classification with hand-designed or machine-designed texture descriptors: a performance evaluation. In: Rojas, I., Ortuño, F. (eds.) Bioinformatics and Biomedical Engineering, vol. 10814, pp. 266–275. Springer, Cham (2018)

    Chapter  Google Scholar 

  4. Cascianelli, S., Bello-Cerezo, R., Bianconi, F., Fravolini, M.L., Belal, M., Palumbo, B., Kather, J.N.: Dimensionality reduction strategies for CNN-based classification of histopathological images. In: De Pietro, G., Gallo, L., Howlett, R.J., Jain, L.C. (eds.) Intelligent Interactive Multimedia Systems and Services 2017, vol. 76, pp. 21–30. Springer, Cham (2018)

    Chapter  Google Scholar 

  5. Feng, Y., Zhang, L., Mo, J.: Deep manifold preserving autoencoder for classifying breast cancer histopathological images. IEEE/ACM Trans. Comput. Biol. Bioinf. 1 (2018)

    Google Scholar 

  6. Guillén-Rondon, P., Robinson, M., Ebalunode, J.: Breast cancer classification: a deep learning approach for digital pathology. In: Meneses, E., Castro, H., Hernández, C.J.B., Ramos-Pollan, R. (eds.) High Performance Computing, vol. 979, pp. 33–40. Springer, Cham (2019)

    Chapter  Google Scholar 

  7. Zilong, H., Tang, J., Wang, Z., Zhang, K., Zhang, L., Sun, Q.: Deep learning for image-based cancer detection and diagnosis - a survey. Pattern Recogn. 83, 134–149 (2018)

    Article  Google Scholar 

  8. Iqbal, T., Ali, H.: Generative adversarial network for medical images (MI-GAN). J. Med. Syst. 42(11), 231 (2018)

    Google Scholar 

  9. Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-Image Translation with Conditional Adversarial Networks. arXiv:1611.07004 [cs]

  10. Jonnalagedda, P., Schmolze, D., Bhanu, B.: [Regular Paper] MVPNets: multi-viewing path deep learning neural networks for magnification invariant diagnosis in breast cancer. In: 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 189–194. IEEE, Taichung, October 2018

    Google Scholar 

  11. Karras, T., Laine, S., Aila, T.: A Style-Based Generator Architecture for Generative Adversarial Networks. arXiv:1812.04948 [cs, stat], December 2018

  12. Komura, D., Ishikawa, S.: Machine learning methods for histopathological image analysis. Comput. Struct. Biotechnol. J. 16, 34–42 (2018)

    Article  Google Scholar 

  13. Kumar, K., Rao, A.C.S.: Breast cancer classification of image using convolutional neural network. In: 2018 4th International Conference on Recent Advances in Information Technology (RAIT), pp. 1–6. IEEE, Dhanbad, March 2018

    Google Scholar 

  14. Li, L., Pan, X., Yang, H., Liu, Z., He, Y., Li, Z., Fan, Y., Cao, Z., Zhang, L.: Multi-task deep learning for fine-grained classification and grading in breast cancer histopathological images. In: Multimedia Tools and Applications, December 2018

    Google Scholar 

  15. Mahapatra, D., Bozorgtabar, B., Garnavi, R.: Image super-resolution using progressive generative adversarial networks for medical image analysis. Comput. Med. Imaging Graph. 71, 30–39 (2019)

    Article  Google Scholar 

  16. Modak, S.K.S., Jha, V.K.: Multibiometric fusion strategy and its applications: a review. Inf. Fusion 49, 174–204 (2019)

    Article  Google Scholar 

  17. Mukkamala, R., Neeraja, P.S., Pamidi, S., Babu, T., Singh, T.: Deep PCANet framework for the binary categorization of breast histopathology images. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 105–110. IEEE, Bangalore, September 2018

    Google Scholar 

  18. Murtaza, G., Shuib, L., Mujtaba, G., Raza, G.: Breast cancer multi-classification through deep neural network and hierarchical classification approach. Multimed. Tools Appl. 1–31 (2019)

    Google Scholar 

  19. Qi, Q., Li, Y., Wang, J., Zheng, H., Huang, Y., Ding, X., Rohde, G.: Label-efficient breast cancer histopathological image classification. IEEE J. Biomed. Health Inf. 23, 1 (2018)

    Article  Google Scholar 

  20. Reza, M.S., Ma, J.: Imbalanced histopathological breast cancer image classification with convolutional neural network. In: 2018 14th IEEE International Conference on Signal Processing (ICSP), pp. 619–624. IEEE, Beijing, China, August 2018

    Google Scholar 

  21. Roy, K., Banik, D., Bhattacharjee, D., Nasipuri, M.: Patch-based system for classification of breast histology images using deep learning. Comput. Med. Imaging Graph. 71, 90–103 (2019)

    Article  Google Scholar 

  22. Senaras, C., Niazi, M.K.K., Sahiner, B., Pennell, M.P., Tozbikian, G., Lozanski, G., Gurcan, M.N.: Optimized generation of high-resolution phantom images using cGAN: application to quantification of Ki67 breast cancer images. PLoS ONE 13(5), e0196846 (2018)

    Article  Google Scholar 

  23. Shallu, Mehra, R.: Breast cancer histology images classification: training from scratch or transfer learning? ICT Express 4(4), 247–254 (2018)

    Article  Google Scholar 

  24. Shin, H.-C., Tenenholtz, N.A., Rogers, J.K., Schwarz, C.G., Senjem, M.L., Gunter, J.L., Andriole, K.P., Michalski, M.: Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In: Gooya, A., Goksel, O., Oguz, I., Burgos, N. (eds.) Simulation and Synthesis in Medical Imaging, vol. 11037, pp. 1–11. Springer, Cham (2018)

    Chapter  Google Scholar 

  25. Song, Y., Chang, H., Gao, Y., Liu, S., Zhang, D., Yao, J., Chrzanowski, W., Cai, W.: Feature learning with component selective encoding for histopathology image classification. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (2018)

    Google Scholar 

  26. Song, Y., Chang, H., Huang, H., Cai, W.: Supervised Intra-embedding of fisher vectors for histopathology image classification. In: Han, Y. (ed.) Physics and Engineering of Metallic Materials, vol. 217, pp. 99–106. Springer, Singapore (2017)

    Google Scholar 

  27. Spanhol, F.A., Oliveira, L.S., Cavalin, P.R., Petitjean, C., Heutte, L.: Deep features for breast cancer histopathological image classification. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1868–1873. IEEE, Banff, AB, October 2017

    Google Scholar 

  28. 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)

    Article  Google Scholar 

  29. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using Convolutional Neural Networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2560–2567. IEEE, Vancouver, BC, Canada, July 2016

    Google Scholar 

  30. Sudharshan, P.J., Petitjean, C., Spanhol, F., Oliveira, L.E., Heutte, L., Honeine, P.: Multiple instance learning for histopathological breast cancer image classification. Expert Syst. Appl. 117, 103–111 (2019)

    Article  Google Scholar 

  31. Vo, D.M., Nguyen, N.-Q., Lee, S.-W.: Classification of breast cancer histology images using incremental boosting convolution networks. Inf. Sci. 482, 123–138 (2019)

    Article  Google Scholar 

  32. Weiss, N., Kost, H., Homeyer, A.: Towards interactive breast tumor classification using transfer learning. In: Campilho, A., Karray, F., Romeny, B.H. (eds.) Image Analysis and Recognition, vol. 10882, pp. 727–736. Springer, Cham (2018)

    Google Scholar 

  33. Zhang, G., Xiao, M., Huang,Y.-H.: Histopathological image recognition with domain knowledge based deep features. In: Huang, D.-S., Gromiha, M.M., Han, K., Hussain, A. (eds.), Intelligent Computing Methodologies, vol. 10956, pp. 349–359. Springer, Cham (2018)

    Chapter  Google Scholar 

  34. Zhang, X., Zhang, Y., Qian, B., Liu, X., Li, X., Wang, X., Yin, C., Lv, X., Song, L., Wang, L.: Classifying breast cancer histopathological images using a robust artificial neural network architecture. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds.) Bioinformatics and Biomedical Engineering, vol. 11466, pp. 204–215. Springer, Cham (2019)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam

    Mai Bui Huynh Thuy & Vinh Truong Hoang

Authors
  1. Mai Bui Huynh Thuy
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  2. Vinh Truong Hoang
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Corresponding author

Correspondence to Vinh Truong Hoang .

Editor information

Editors and Affiliations

  1. Computer Science and Applications Department LGIPM, University of Lorraine, Metz Cedex 03, France

    Hoai An Le Thi

  2. Computer Science and Applications Department LGIPM, University of Lorraine, Metz Cedex 03, France

    Hoai Minh Le

  3. Laboratory of Mathematics, National Institute for Applied Sciences, Saint-Étienne-du-Rouvray Cedex, France

    Tao Pham Dinh

  4. Department of Information Systems, Wroclaw University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

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Thuy, M.B.H., Hoang, V.T. (2020). Fusing of Deep Learning, Transfer Learning and GAN for Breast Cancer Histopathological Image Classification. In: Le Thi, H., Le, H., Pham Dinh, T., Nguyen, N. (eds) Advanced Computational Methods for Knowledge Engineering. ICCSAMA 2019. Advances in Intelligent Systems and Computing, vol 1121. Springer, Cham. https://doi.org/10.1007/978-3-030-38364-0_23

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