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Breast Cancer Histopathology Image Classification Using Frequency Attention Convolution Network

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Advanced Data Mining and Applications (ADMA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14177))

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Abstract

The existing deep learning works mainly capture breast cancer histopathology image features in the spatial domain, and they rarely consider the frequency domain feature representation of histopathology images. According to the classical digital signal processing theory, frequency domain features may outperform spatial domain features in analyzing texture images. Motivated by this, we attempt to mine frequency domain features for the breast cancer histopathology image classification application, and further propose a novel frequency-attention convolutional network called SeFFT-Net by combining the Fourier transform with the channel attention mechanism. The core of SeFFT-Net consists of a newly constructed frequency-based squeeze and excitation (SeFFT) module, which first performs Fourier transform with residual construction to capture deep features in the frequency domain of histopathology images, followed by a squeeze-and-excitation attention operator to further enhance important frequency features. We extensively evaluate the proposed SeFFT-Net model on the public BreakHis breast cancer histopathology dataset, and it achieves the optimal image-level and patient-level classification accuracy of 98.67% and 98.16%, respectively. Meanwhile, ablation studies also well demonstrate the effectiveness of introducing frequency transforms for this medical image application.

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References

  1. Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 68(6), 394–424 (2018)

    Google Scholar 

  2. Joy, J.E., Penhoet, E.E., Petitti, D.B., Ebrary, I.: Saving women’s lives: strategies for improving breast cancer detection and diagnosis. J. Laryngol. Otol. 86(2), 105–19 (2005)

    Google Scholar 

  3. Gupta, V., Bhavsar, A.: Sequential modeling of deep features for breast cancer histopathological image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops(CVPRW), Salt Lake City, UT, USA, pp. 2254–2261 (2018). https://doi.org/10.1109/CVPRW.2018.00302

  4. Chhipa, P.C., Upadhyay, R., Pihlgren, G.G., Saini, R., Uchida, S., Liwicki, M.: Magnification prior: a self-supervised method for learning representations on breast cancer histopathological images. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2717–2727 (2023). https://doi.org/10.48550/arXiv.2203.07707

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

    Article  Google Scholar 

  6. Chukwu, J.K., Sani, F.B., Nuhu, A.S.: Breast cancer classification using deep convolutional neural networks. FUOYE J. Eng. Technol. 6(2), 35–38 (2021)

    Article  Google Scholar 

  7. 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), Banff, AB, Canada, pp. 1868–1873 (2017). https://doi.org/10.1109/SMC.2017.8122889

  8. Deniz, E., Şengür, A., Kadiroǧlu, Z., Guo, Y., Bajaj, V., Budak, Ü.: Transfer learning based histopathologic image classification for breast cancer detection. Health Inf. Sci. Syst. 6(1), 1–7 (2018)

    Article  Google Scholar 

  9. Jiang, Y., Chen, L., Zhang, H., Xiao, X.: Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PloS One 14(3), e0214587 (2019)

    Article  Google Scholar 

  10. Sohail, A., Khan, A., Wahab, N., Zameer, A., Khan, S.: A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images. Sci. Rep. 11(1), 1–18 (2021)

    Article  Google Scholar 

  11. Juppet, Q., De Martino, F., Marcandalli, E., Weigert, M., Burri, O., Unser, M.: Deep learning enables individual xenograft cell classification in histological images by analysis of contextual features. J. Mammary Gland Biol. Neoplasia 26(2), 101–112 (2021)

    Article  Google Scholar 

  12. Hirra, I., Ahmad, M., Hussain, A., Ashraf, M.U., Saeed, I.A., Qadri, S.F.: Breast cancer classification from histopathological images using patch-based deep learning modeling. IEEE Access 9, 24273–24287 (2021)

    Article  Google Scholar 

  13. Gueguen, L., Sergeev, A., Kadlec, B., Liu, R., Yosinski, J.: Faster neural networks straight from JPEG. In: 32nd Conference on Neural Information Processing Systems, pp. 1–13 (2018)

    Google Scholar 

  14. Ehrlich, M., Davis, L. S.: Deep residual learning in the JPEG transform domain. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, pp. 3484–3493 (2019). https://doi.org/10.1109/ICCV.2019.00358

  15. Zhong, Y., Li, B., Tang, L., Kuang, S., Wu, S., Ding, S.: Detecting camouflaged object in frequency domain. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, pp. 4504–4513 (2022). https://doi.org/10.1109/CVPR52688.2022.00446

  16. Hu, J., Shen, L., Sun, G.J., Albanie, S., Wu, E.H., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141. (2018). https://doi.org/10.48550/arXiv.1709.01507

  17. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2015). 10.48550/arXiv.1512.03385

    Google Scholar 

  18. Wang, K.N., He, Y., Zhuang, S., Miao, J., He, X., Zhou, P.: FFCNet: fourier transform-based frequency learning and complex convolutional network for colon disease classification. In: Proceedings of the 25th International Conference of Medical Image Computing and Computer Assisted Intervention-MICCAI 2022: 25th International Conference, Singapore, 18–22 September 2022, Proceedings, Part III, pp. 78–87 (2022). https://doi.org/10.1007/978-3-031-16437-8_8

  19. Zhang, J., Wei, X., Dong, J., Liu, B.: Aggregated deep global feature representation for breast cancer histopathology image classification. J. Med. Imaging Health Inf. 10(11), 2778–2783 (2020)

    Article  Google Scholar 

  20. Lichtblau, D., Stoean, C., Magalhaes, M.: Cancer diagnosis through a tandem of classifiers for digitized histopathological slides. PLoS ONE 14(1), e0209274 (2019)

    Article  Google Scholar 

  21. Hou, Y.: Breast cancer pathological image classification based on deep learning. J. Xray Sci. Technol. 28(4), 727–738 (2020)

    Google Scholar 

  22. Saxena, S., Shukla, S., Gyanchandani, M.: Breast cancer histopathology image classification using kernelized weighted extreme learning machine. Int. J. Imaging Syst. Technol. 31(1), 168–179 (2021)

    Article  Google Scholar 

  23. Han, Z., Wei, B., Zheng, Y., Yin, Y., Li, K., Li, S.: Breast cancer multi-classification from histopathological images with structured deep learning model. Sci. Rep. 7(1), 4172 (2017)

    Article  Google Scholar 

  24. 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. Digit. Imaging 32(5), 605–617 (2019)

    Article  Google Scholar 

  25. Hao, Y., Zhang, L., Qiao, S., Bai, Y., Cheng, R., Xue, H.: Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix. Plos One 17(5), e0267955 (2022)

    Article  Google Scholar 

  26. Boumaraf, S., Liu, X., Wan, Y., Zheng, Z., Ferkous, C., Ma, X.: Conventional machine learning versus deep learning for magnification dependent histopathological breast cancer image classification: a comparative study with visual explanation. Diagnostics 11(3), 528 (2021)

    Article  Google Scholar 

  27. Li, X., Li, H., Cui, W., Cai, Z., Jia, M.: Classification on digital pathological images of breast cancer based on deep features of different levels. Math. Prob. Eng. 2021, 1–13 (2021)

    Article  Google Scholar 

  28. Man, R., Yang, P., Xu, B.: Classification of breast cancer histopathological images using discriminative patches screened by generative adversarial networks. IEEE Access 8, 155362–155377 (2020)

    Article  Google Scholar 

  29. Sharma, S., Kumar, S.: The Xception model: a potential feature extractor in breast cancer histology images classification. ICT Exp. 8(1), 101–108 (2022)

    Article  Google Scholar 

  30. Xu, Y., dos Santos, M.A., Souza, L.F.F., Marques, A.G., Zhang, L., da Costa Nascimento, J.J.: New fully automatic approach for tissue identification in histopathological examinations using transfer learning. IET Image Process. 16(11), 2875–2889 (2022)

    Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61972062, the Applied Basic Research Project of Liaoning Province under Grant 2023JH2/101300191 and 2023JH2/101300193.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Dalian Minzu University, Dalian, China

    Ruidong Lu, Xueyan Ding & Jianxin Zhang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Qiule Sun

  3. SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University, Dalian, China

    Jianxin Zhang

Authors
  1. Ruidong Lu
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  2. Qiule Sun
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  3. Xueyan Ding
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  4. Jianxin Zhang
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Corresponding author

Correspondence to Jianxin Zhang .

Editor information

Editors and Affiliations

  1. Northeastern University, Shenyang, China

    Xiaochun Yang

  2. The University of Indonesia, Depok, Indonesia

    Heru Suhartanto

  3. Beijing Institute of Technology, Beijing, China

    Guoren Wang

  4. Northeastern University, Shenyang, China

    Bin Wang

  5. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  6. Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Bing Li

  7. Sun Yat-sen University, Guangzhou, China

    Huaijie Zhu

  8. Anhui University, Hefei, China

    Ningning Cui

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Lu, R., Sun, Q., Ding, X., Zhang, J. (2023). Breast Cancer Histopathology Image Classification Using Frequency Attention Convolution Network. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14177. Springer, Cham. https://doi.org/10.1007/978-3-031-46664-9_15

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  • DOI: https://doi.org/10.1007/978-3-031-46664-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46663-2

  • Online ISBN: 978-3-031-46664-9

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