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Transformer Based Multi-view Network for Mammographic Image Classification

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 (MICCAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13433))

Abstract

Most of the existing multi-view mammographic image analysis methods adopt a simple fusion strategy: features concatenation, which is widely used in many features fusion methods. However, concatenation based methods can’t extract cross view information very effectively because different views are likely to be unaligned. Recently, many researchers have attempted to introduce attention mechanism related methods into the field of multi-view mammography analysis. But these attention mechanism based methods still partly rely on convolution, so they can’t take full advantages of attention mechanism. To take full advantage of multi-view information, we propose a novel pure transformer based multi-view network to solve the question of mammographic image classification. In our primary network, we use a transformer based backbone network to extract image features, a “cross view attention block” structure to fuse multi-view information, and a “classification token” to gather all useful information to make the final prediction. Besides, we compare the performance when fusing multi-view information at different stages of the backbone network using a novel designed “(shifted) window based cross view attention block” structure and compare the results when fusing different views’ information. The results on DDSM dataset show that our networks can effectively use multi-view information to make judgments and outperform the concatenation and convolution based methods.

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References

  1. Sun, L., Wang, J., Hu, Z., Xu, Y., Cui, Z.: Multi-view convolutional neural networks for mammographic image classification. IEEE Access 7, 126273–126282 (2019)

    Article  Google Scholar 

  2. Nasir Khan, H., Shahid, A.R., Raza, B., Dar, A.H., Alquhayz, H.: Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access 7, 165724–165733 (2019)

    Article  Google Scholar 

  3. Wu, N., et al.: Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging 39, 1184–1194 (2020)

    Article  Google Scholar 

  4. Li, C., et al.: Multi-view mammographic density classification by dilated and attention-guided residual learning. IEEE/ACM Trans. Comput. Biol. Bioinf. 18, 1003–1013 (2021)

    Article  Google Scholar 

  5. Zhao, X., Yu, L., Wang, X.: Cross-view attention network for breast cancer screening from multi-view mammograms. In: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2020, pp. 1050–1054. IEEE, Barcelona (2020)

    Google Scholar 

  6. Hu, H., Gu, J., Zhang, Z., Dai, J., Wei, Y.: Relation networks for object detection. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3588–3597. IEEE, Salt Lake City (2018)

    Google Scholar 

  7. Ma, J., Li, X., Li, H., Wang, R., Menze, B., Zheng, W.-S.: Cross-view relation networks for mammogram mass detection. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 8632–8638. IEEE, Milan (2021)

    Google Scholar 

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

    Article  Google Scholar 

  9. van Tulder, G., Tong, Y., Marchiori, E.: Multi-view analysis of unregistered medical images using cross-view transformers. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 104–113. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87199-4_10

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  11. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30, pp. 5998–6008 (2017)

    Google Scholar 

  12. Dosovitskiy, A., et al.: An image is worth 16 × 16 words: transformers for image recognition at scale. In: ICLR (2021)

    Google Scholar 

  13. Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: ICCV (2021)

    Google Scholar 

  14. Tsai, Y.-H.H., Bai, S., Liang, P.P., Kolter, J.Z., Morency, L.-P., Salakhutdinov, R.: Multimodal transformer for unaligned multimodal language sequences. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 6558–6569. Association for Computational Linguistics, Florence (2019)

    Google Scholar 

  15. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. IEEE, Las Vegas (2016)

    Google Scholar 

  16. Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, P., Moore, R., Chang, K.: 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

  17. Yan, Y., Conze, P.-H., Lamard, M., Quellec, G., Cochener, B., Coatrieux, G.: Towards improved breast mass detection using dual-view mammogram matching. Med. Image Anal. 71, 102083 (2021)

    Article  Google Scholar 

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Acknowledgement

This research is supported by Zhengzhou collaborative innovation major special project (20XTZX11020).

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Correspondence to Huiqin Jiang .

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Sun, Z., Jiang, H., Ma, L., Yu, Z., Xu, H. (2022). Transformer Based Multi-view Network for Mammographic Image Classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13433. Springer, Cham. https://doi.org/10.1007/978-3-031-16437-8_5

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  • DOI: https://doi.org/10.1007/978-3-031-16437-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16436-1

  • Online ISBN: 978-3-031-16437-8

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