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Bilateral Mammogram Mass Detection Based on Window Cross Attention

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Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 14257))

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Abstract

Breast cancer is the most common cancer in the world. Mammogram mass detection aids in the early detection of breast cancer and increases patient survival rates. Because the bilateral breasts of the same patient are similar and symmetrical, information fusion of bilateral mammogram images is advantageous in improving the detection rate of masses. However, existing mass detection methods use pixel-level corresponding feature fusion methods, which are sensitive to image registration errors. In this paper, we propose WCA-RCNN, a novel mass detection framework that uses window cross attention to fuse information from bilateral mammogram images. The window cross attention module eliminates pixel-level correspondence and significantly improves mass detection accuracy. In addition, we propose a mass deformation data augmentation method to address the issue of insufficient mass samples. We evaluate the proposed method on the publicly available mammography dataset DDSM, demonstrating that it outperforms state-of-the-art mass detection methods.

This work is supported by the National Natural Science Foundation of China General Program 61976239.

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References

  1. Aly, G.H., Marey, M., El-Sayed, S.A., Tolba, M.F.: YOLO based breast masses detection and classification in full-field digital mammograms. Comput. Methods Programs Biomed.200, 105823 (2021)

    Google Scholar 

  2. Cao, H., Pu, S., Tan, W., Tong, J.: Breast mass detection in digital mammography based on anchor-free architecture. Comput. Methods Programs Biomed.205, 106033 (2021)

    Google Scholar 

  3. Cao, Z., et al.: DeepLIMa: deep learning based lesion identification in mammograms. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 362–370 (2019)

    Google Scholar 

  4. Debelee, T.G., Schwenker, F., Ibenthal, A., Yohannes, D.: Survey of deep learning in breast cancer image analysis. Evol. Syst.11, 143–163 (2020)

    Article  Google Scholar 

  5. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009).https://doi.org/10.1109/CVPR.2009.5206848

  6. Dhungel, N., Carneiro, G., Bradley, A.P.: A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med. Image Anal.37, 114–128 (2017)

    Article  Google Scholar 

  7. Gardezi, S.J.S., Elazab, A., Lei, B., Wang, T.: Breast cancer detection and diagnosis using mammographic data: systematic review. J. Med. Internet Res.21(7), e14464 (2019)

    Article  Google Scholar 

  8. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  9. 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 (CVPR) (2016)

    Google Scholar 

  10. Heath, M., et al.: Current status of the digital database for screening mammography. In: Karssemeijer, N., Thijssen, M., Hendriks, J., van Erning, L. (eds.) Digital Mammography. Computational Imaging and Vision, vol. 13, pp. 457–460. Springer, Dordrecht (1998).https://doi.org/10.1007/978-94-011-5318-8_75

    Chapter  Google Scholar 

  11. Hu, H., Gu, J., Zhang, Z., Dai, J., Wei, Y.: Relation networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3588–3597 (2018)

    Google Scholar 

  12. Jalalian, A., Mashohor, S.B., Mahmud, H.R., Saripan, M.I.B., Ramli, A.R.B., Karasfi, B.: Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin. Imaging37(3), 420–426 (2013)

    Article  Google Scholar 

  13. Jung, H., et al.: Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PLoS ONE13(9), e0203355 (2018)

    Article  Google Scholar 

  14. Li, Y., Zhang, L., Chen, H., Cheng, L.: Mass detection in mammograms by bilateral analysis using convolution neural network. Comput. Methods Programs Biomed.195, 105518 (2020)

    Google Scholar 

  15. Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  16. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal.42, 60–88 (2017)

    Article  Google Scholar 

  17. Liu, Y., Zhang, F., Chen, C., Wang, S., Wang, Y., Yu, Y.: Act like a radiologist: towards reliable multi-view correspondence reasoning for mammogram mass detection. IEEE Trans. Pattern Anal. Mach. Intell.44(10), 5947–5961 (2022).https://doi.org/10.1109/TPAMI.2021.3085783

    Article  Google Scholar 

  18. Liu, Y., Zhang, F., Zhang, Q., Wang, S., Wang, Y., Yu, Y.: Cross-view correspondence reasoning based on bipartite graph convolutional network for mammogram mass detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  19. Liu, Y., et al.: Compare and contrast: detecting mammographic soft-tissue lesions with C2-Net. Med. Image Anal.71, 101999 (2021)

    Google Scholar 

  20. Liu, Y., et al.: From unilateral to bilateral learning: detecting mammogram masses with contrasted bilateral network. In: Shen, D., et al. (eds.) MICCAI 2019, Part VI. LNCS, vol. 11769, pp. 477–485. Springer, Cham (2019).https://doi.org/10.1007/978-3-030-32226-7_53

    Chapter  Google Scholar 

  21. Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)

    Google Scholar 

  22. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  23. Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: INbreast: toward a full-field digital mammographic database. Acad. Radiol.19(2), 236–248 (2012).https://doi.org/10.1016/j.acra.2011.09.014,https://www.sciencedirect.com/science/article/pii/S107663321100451Xhttps://www.sciencedirect.com/science/article/pii/S107663321100451X

  24. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern.9(1), 62–66 (1979)

    Article  Google Scholar 

  25. Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019).https://proceedings.neurips.cc/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf

  26. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)

    Google Scholar 

  27. Simard, P.Y., Steinkraus, D., Platt, J.C., et al.: Best practices for convolutional neural networks applied to visual document analysis. In: ICDAR, vol. 3. Edinburgh (2003)

    Google Scholar 

  28. Suckling, J.: The mammographic images analysis society digital mammogram database. In: Exerpta Medica. International Congress Series 1994. vol. 1069, pp. 375–378 (1994)

    Google Scholar 

  29. Sun, Y.S., et al.: Risk factors and preventions of breast cancer. Int. J. Biol. Sci.13(11), 1387 (2017)

    Article  Google Scholar 

  30. Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin.71(3), 209–249 (2021)

    Google Scholar 

  31. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  32. Yang, Z., et al.: MommiNet-v2: mammographic multi-view mass identification networks. Med. Image Anal.73, 102204 (2021)

    Google Scholar 

  33. Zhao, Z.Q., Zheng, P., Xu, S.T., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst.30(11), 3212–3232 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Communication and Computer Network, South China University of Technology, Guangzhou, 510006, China

    Hua Yuan, YiMao Yan & Shoubin Dong

Authors
  1. Hua Yuan

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  2. YiMao Yan

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  3. Shoubin Dong

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Correspondence toYiMao Yan.

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  3. Lancaster University, Lancaster, UK

    Plamen Angelov

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

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Yuan, H., Yan, Y., Dong, S. (2023). Bilateral Mammogram Mass Detection Based on Window Cross Attention. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. https://doi.org/10.1007/978-3-031-44216-2_6

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