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Dual-Domain Learning Network for Polyp Segmentation

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Digital Forensics and Watermarking (IWDW 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14511))

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Abstract

Automatic polyp segmentation is a crucial application of artificial intelligence in the medical field. However, this task is challenging due to uneven brightness, variable colors, and blurry boundaries. Most current polyp segmentation methods focus on features extracted from the spatial domain, ignoring the valuable information contained in the frequency domain. In this paper, we propose a Dual-Domain Learning Network (D\(^{2}\)LNet) for polyp segmentation. Specifically, we propose a Phase-Amplitude Attention Module, which enhances the details in the phase spectrum, while reducing interference from brightness and color in the amplitude spectrum. Moreover, we introduce a Spatial-Frequency Fusion Module that utilizes parameterized frequency-domain features to adjust the style of spatial-domain features and improve polyp visibility. Extensive experiments demonstrate that our method outperforms the state-of-the-art approaches both visually and quantitatively.

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References

  1. Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., et al.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. CMIG 43, 99–111 (2015)

    Google Scholar 

  2. Chi, L., Jiang, B., Mu, Y.: Fast Fourier convolution. In: Advances in Neural Information Processing Systems, vol. 33, pp. 4479–4488 (2020)

    Google Scholar 

  3. Dong, B., Wang, W., Fan, D.P., Li, J., Fu, H., Shao, L.: Polyp-PVT: polyp segmentation with pyramid vision transformers. arXiv preprint arXiv:2108.06932 (2021)

  4. Duc, N.T., Oanh, N.T., Thuy, N.T., Triet, T.M., Dinh, V.S.: ColonFormer: an efficient transformer based method for colon polyp segmentation. IEEE Access 10, 80575–80586 (2022)

    Article  Google Scholar 

  5. Fan, D.P., Cheng, M.M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4548–4557. IEEE (2017)

    Google Scholar 

  6. Fan, D.P., Gong, C., Cao, Y., et al.: Enhanced-alignment measure for binary foreground map evaluation. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization (2018)

    Google Scholar 

  7. Fan, D.-P., et al.: PraNet: parallel reverse attention network for polyp segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 263–273. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_26

    Chapter  Google Scholar 

  8. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141. IEEE (2018)

    Google Scholar 

  9. Jha, D., et al.: Kvasir-SEG: a segmented polyp dataset. In: Ro, Y., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 451–462. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37734-2_37

    Chapter  Google Scholar 

  10. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR 2019. OpenReview.net (2018)

    Google Scholar 

  11. Margolin, R., Zelnik-Manor, L., Tal, A.: How to evaluate foreground maps? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2014)

    Google Scholar 

  12. Oppenheim, A.V., Lim, J.S.: The importance of phase in signals. Proc. IEEE 69(5), 529–541 (1981)

    Article  Google Scholar 

  13. Patel, K., Bur, A.M., Wang, G.: Enhanced U-Net: a feature enhancement network for polyp segmentation. In: 2021 18th Conference on Robots and Vision (CRV), pp. 181–188. IEEE (2021)

    Google Scholar 

  14. Poudel, S., Lee, S.W.: Deep multi-scale attentional features for medical image segmentation. Appl. Soft Comput. 109, 107445 (2021)

    Article  Google Scholar 

  15. Ren, J., Hu, X., Zhu, L., et al.: Deep texture-aware features for camouflaged object detection. IEEE Trans. Circ. Syst. Video Technol. 33(3), 1157–1167 (2023)

    Article  Google Scholar 

  16. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  17. Shussman, N., Wexner, S.D.: Colorectal polyps and polyposis syndromes. Gastroenterology Rep. 2(1), 1–15 (2014)

    Article  Google Scholar 

  18. Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. CARS 9, 283–293 (2014)

    Article  Google Scholar 

  19. Skarbnik, N., Zeevi, Y.Y., Sagiv, C.: The importance of phase in image processing. Technion-Israel Institute of Technology, Faculty of Electrical Engineering (2009)

    Google Scholar 

  20. Suvorov, R., Logacheva, E., Mashikhin, A., et al.: Resolution-robust large mask inpainting with Fourier convolutions. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2149–2159. IEEE (2022)

    Google Scholar 

  21. Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE TMI 35(2), 630–644 (2015)

    Google Scholar 

  22. Vázquez, D., Bernal, J., Sánchez, F.J., et al.: A benchmark for endoluminal scene segmentation of colonoscopy images. JHE 2017, 9 (2017)

    Google Scholar 

  23. Wang, J., Huang, Q., Tang, F., Meng, J., Su, J., Song, S.: Stepwise feature fusion: local guides global. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13433, pp. 110–120. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16437-8_11

    Chapter  Google Scholar 

  24. Wang, K.N., et al.: FFCNet: Fourier transform-based frequency learning and complex convolutional network for colon disease classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13433, pp. 78–87. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16437-8_8

    Chapter  Google Scholar 

  25. Wang, W., Xie, E., Li, X., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the International Conference on Computer Vision, pp. 568–578. IEEE (2021)

    Google Scholar 

  26. Wang, W., Xie, E., Li, X., et al.: PVT v2: improved baselines with pyramid vision transformer. Comput. Vis. Media 8(3), 415–424 (2022)

    Article  Google Scholar 

  27. Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S.K., Cui, S.: Shallow attention network for polyp segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 699–708. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_66

    Chapter  Google Scholar 

  28. Wei, J., Wang, S., Huang, Q.: F\(^3\)Net: fusion, feedback and focus for salient object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12321–12328. AAAI Press (2020)

    Google Scholar 

  29. Wu, H., Zhong, J., Wang, W., Wen, Z., Qin, J.: Precise yet efficient semantic calibration and refinement in convnets for real-time polyp segmentation from colonoscopy videos. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2916–2924. AAAI Press (2021)

    Google Scholar 

  30. Yin, Z., Liang, K., Ma, Z., Guo, J.: Duplex contextual relation network for polyp segmentation. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–5. IEEE (2022)

    Google Scholar 

  31. Yu, H., Zheng, N., Zhou, M., Huang, J., Xiao, Z., Zhao, F.: Frequency and spatial dual guidance for image dehazing. In: Avidan, S., Brostow, G.J., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13679, pp. 181–198. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19800-7_11

    Chapter  Google Scholar 

  32. Zhang, R., Li, G., Li, Z., Cui, S., Qian, D., Yu, Y.: Adaptive context selection for polyp segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 253–262. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_25

    Chapter  Google Scholar 

  33. Zhao, X., Zhang, L., Lu, H.: Automatic polyp segmentation via multi-scale subtraction network. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 120–130. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_12

    Chapter  Google Scholar 

  34. Zhou, T., et al.: Cross-level feature aggregation network for polyp segmentation. Pattern Recogn. 140, 109555 (2023)

    Article  Google Scholar 

  35. Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: StoyanovD, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1

    Chapter  Google Scholar 

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Acknowledgements

This work was supported by the Shenzhen Science and Technology Program (Grant No. JCYJ20220530145209022), Chinese Academy of Sciences Cyber Security and Informatization Project (No. CAS-WX2022SF-0102), and Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX22_0461).

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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100195, China

    Yan Li

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100195, China

    Yan Li

  3. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Zhuoran Zheng & Xiuyi Jia

  4. School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen, 510006, China

    Wenqi Ren

  5. Brussel Photonics, Department of Applied Physics and Photonics, Vrije Universiteit Brussel and Flanders Make, 1050, Brussels, Belgium

    Yunfeng Nie

  6. School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100039, China

    Jingang Zhang

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  1. Yan Li
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  2. Zhuoran Zheng
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  3. Wenqi Ren
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  4. Yunfeng Nie
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  6. Xiuyi Jia
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Correspondence to Wenqi Ren .

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Editors and Affiliations

  1. School of Cyber Security, Qilu University of Technology, Jinan, China

    Bin Ma

  2. Qilu University of Technology, Jinan, China

    Jian Li

  3. Qilu University of Technology, Jinan, China

    Qi Li

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Li, Y., Zheng, Z., Ren, W., Nie, Y., Zhang, J., Jia, X. (2024). Dual-Domain Learning Network for Polyp Segmentation. In: Ma, B., Li, J., Li, Q. (eds) Digital Forensics and Watermarking. IWDW 2023. Lecture Notes in Computer Science, vol 14511. Springer, Singapore. https://doi.org/10.1007/978-981-97-2585-4_17

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  • DOI: https://doi.org/10.1007/978-981-97-2585-4_17

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  • Online ISBN: 978-981-97-2585-4

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