Abstract
Colonoscopy images from different centres usually exhibit appearance variations, making the models trained on one domain unable to generalize well to another. To tackle this issue, we propose a novel Task-relevant Feature Replenishment based Network (TRFR-Net) for cross-centre polyp segmentation via retrieving task-relevant knowledge for sufficient discrimination capability with style variations alleviated. Specifically, we first design a domain-invariant feature decomposition (DIFD) module placed after each encoding block to extract domain-shared information for segmentation. Then we develop a task-relevant feature replenishment (TRFR) module to distill informative context from the residual features of each DIFD module and dynamically aggregate these task-relevant parts, providing extra information for generalized segmentation learning. To further bridge the domain gap leveraging structural similarity, we devise a Polyp-aware Adversarial Learning (PPAL) module to align prediction feature distribution, where more emphasis is imposed on the polyp-related alignment. Experimental results on three public datasets demonstrate the effectiveness of our proposed algorithm. The code is available at: https://github.com/CathyS1996/TRFRNet.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, J., Li, Y., Ma, K., Zheng, Y.: Generative adversarial networks for video-to-video domain adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3462–3469 (2020)
Diao, L., Guo, H., Zhou, Y., He, Y.: Bridging the gap between outputs: domain adaptation for lung cancer IHC segmentation. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 6–10. IEEE (2021)
Dong, J., Cong, Y., Sun, G., Zhong, B., Xu, X.: What can be transferred: unsupervised domain adaptation for endoscopic lesions segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4023–4032 (2020)
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030–2096 (2016)
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 (2016)
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 (2018)
Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1501–1510 (2017)
Jha, D., et al.: Kvasir-SEG: a segmented polyp dataset. In: Ro, Y.M., 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
Kim, S.Y., et al.: Colonoscopy versus fecal immunochemical test for reducing colorectal cancer risk: a population-based case-control study. Clin. Transl. Gastroenterol. 12(5), e00350 (2021)
Luo, Y., Zheng, L., Guan, T., Yu, J., Yang, Y.: Taking a closer look at domain shift: category-level adversaries for semantics consistent domain adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2507–2516 (2019)
Pan, F., Shin, I., Rameau, F., Lee, S., Kweon, I.S.: Unsupervised intra-domain adaptation for semantic segmentation through self-supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3764–3773 (2020)
Pan, X., Luo, P., Shi, J., Tang, X.: Two at once: enhancing learning and generalization capacities via ibn-net. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 464–479 (2018)
Siegel, R.L., Miller, K.D., Fuchs, H.E., Jemal, A.: Cancer statistics. CA: Can. J. Clin. (2022)
Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9(2), 283–293 (2014)
Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans. Med. Imaging 35(2), 630–644 (2015)
Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H., Chandraker, M.: Learning to adapt structured output space for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7472–7481 (2018)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization (2016)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)
Wang, Y., et al.: Domain-specific suppression for adaptive object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9603–9612 (2021)
Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Mixstyle neural networks for domain generalization and adaptation. arXiv:2107.02053 (2021)
Acknowledgements
This work was supported by National Key R &D Program of China with Grant No.2019YFB1312400, Hong Kong RGC CRF Grant C4063-18G and Hong Kong RGC GRF Grant # 14211420.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shen, Y., Lu, Y., Jia, X., Bai, F., Meng, M.QH. (2022). Task-Relevant Feature Replenishment for Cross-Centre Polyp Segmentation. 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 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_57
Download citation
DOI: https://doi.org/10.1007/978-3-031-16440-8_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16439-2
Online ISBN: 978-3-031-16440-8
eBook Packages: Computer ScienceComputer Science (R0)
