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.
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)