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Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels

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


Noisy labels collected with limited annotation cost prevent medical image segmentation algorithms from learning precise semantic correlations. Previous segmentation arts of learning with noisy labels merely perform a pixel-wise manner to preserve semantics, such as pixel-wise label correction, but neglect the pair-wise manner. In fact, we observe that the pair-wise manner capturing affinity relations between pixels can greatly reduce the label noise rate. Motivated by this observation, we present a novel perspective for noisy mitigation by incorporating both pixel-wise and pair-wise manners, where supervisions are derived from noisy class and affinity labels, respectively. Unifying the pixel-wise and pair-wise manners, we propose a robust Joint Class-Affinity Segmentation (JCAS) framework to combat label noise issues in medical image segmentation. Considering the affinity in pair-wise manner incorporates contextual dependencies, a differentiated affinity reasoning (DAR) module is devised to rectify the pixel-wise segmentation prediction by reasoning about intra-class and inter-class affinity relations. To further enhance the noise resistance, a class-affinity loss correction (CALC) strategy is designed to correct supervision signals via the modeled noise label distributions in class and affinity labels. Meanwhile, CALC strategy interacts the pixel-wise and pair-wise manners through the theoretically derived consistency regularization. Extensive experiments under both synthetic and real-world noisy labels corroborate the efficacy of the proposed JCAS framework with a minimum gap towards the upper bound performance. The source code is available at

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  1. 1.

    Real-world label noises can be well approximated via class-dependent noises [4, 6, 11].


  1. Allan, M., et al.: 2018 robotic scene segmentation challenge. arXiv preprint arXiv:2001.11190 (2020)

  2. Allan, M., et al.: 2017 robotic instrument segmentation challenge. arXiv preprint arXiv:1902.06426 (2019)

  3. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  4. Cheng, J., Liu, T., Ramamohanarao, K., Tao, D.: Learning with bounded instance and label-dependent label noise. In: ICML, pp. 1789–1799. PMLR (2020)

    Google Scholar 

  5. González, C., Bravo-Sánchez, L., Arbelaez, P.: ISINet: an instance-based approach for surgical instrument segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 595–605. Springer, Cham (2020).

    Chapter  Google Scholar 

  6. Guo, X., Liu, J., Liu, T., Yuan, Y.: Simt: handling open-set noise for domain adaptive semantic segmentation. In: CVPR (2022)

    Google Scholar 

  7. Guo, X., Yang, C., Li, B., Yuan, Y.: Metacorrection: domain-aware meta loss correction for unsupervised domain adaptation in semantic segmentation. In: CVPR, pp. 3927–3936 (2021)

    Google Scholar 

  8. Karimi, D., Dou, H., Warfield, S.K., Gholipour, A.: Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis. Med. Image Anal. 65, 101759 (2020)

    Article  Google Scholar 

  9. Karimi, D., Vasylechko, S.D., Gholipour, A.: Convolution-free medical image segmentation using transformers. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 78–88. Springer, Cham (2021).

    Chapter  Google Scholar 

  10. Li, S., Gao, Z., He, X.: Superpixel-guided iterative learning from noisy labels for medical image segmentation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 525–535. Springer, Cham (2021).

    Chapter  Google Scholar 

  11. Li, X., Liu, T., Han, B., Niu, G., Sugiyama, M.: Provably end-to-end label-noise learning without anchor points. In: ICML, pp. 6403–6413 (2021)

    Google Scholar 

  12. Ni, Z.L., Bian, G.B., Hou, Z.G., Zhou, X.H., Xie, X.L., Li, Z.: Attention-guided lightweight network for real-time segmentation of robotic surgical instruments. In: ICRA, pp. 9939–9945. IEEE (2020)

    Google Scholar 

  13. Ni, Z.-L., et al.: RAUNet: residual attention U-Net for semantic segmentation of cataract surgical instruments. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. LNCS, vol. 11954, pp. 139–149. Springer, Cham (2019).

    Chapter  Google Scholar 

  14. Pissas, T., Ravasio, C.S., Cruz, L.D., Bergeles, C.: Effective semantic segmentation in cataract surgery: What matters most? In: MICCAI. pp. 509–518. Springer (2021)

    Google Scholar 

  15. Shu, J., et al.: Meta-weight-net: learning an explicit mapping for sample weighting. In: NeurIPS, pp. 1919–1930 (2019)

    Google Scholar 

  16. Wang, J., Zhou, S., Fang, C., Wang, L., Wang, J.: Meta corrupted pixels mining for medical image segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 335–345. Springer, Cham (2020).

    Chapter  Google Scholar 

  17. Wu, S et al.: Class2simi: a noise reduction perspective on learning with noisy labels. In: ICML, pp. 11285–11295 (2021)

    Google Scholar 

  18. Xu, L., Ouyang, W., Bennamoun, M., Boussaid, F., Sohel, F., Xu, D.: Leveraging auxiliary tasks with affinity learning for weakly supervised semantic segmentation. In: ICCV, pp. 6984–6993 (2021)

    Google Scholar 

  19. Xu, Z., et al.: Noisy labels are treasure: mean-teacher-assisted confident learning for hepatic vessel segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 3–13. Springer, Cham (2021).

    Chapter  Google Scholar 

  20. Zhang, L., et al.: Disentangling human error from ground truth in segmentation of medical images. NeurIPS 33, 15750–15762 (2020)

    Google Scholar 

  21. Zhang, T., Yu, L., Hu, N., Lv, S., Gu, S.: Robust medical image segmentation from non-expert annotations with tri-network. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 249–258. Springer, Cham (2020).

    Chapter  Google Scholar 

  22. Zhang, Z., Zhang, H., Arik, S.O., Lee, H., Pfister, T.: Distilling effective supervision from severe label noise. In: CVPR, pp. 9294–9303 (2020)

    Google Scholar 

  23. Zhou, X., Liu, X., Wang, C., Zhai, D., Jiang, J., Ji, X.: Learning with noisy labels via sparse regularization. In: ICCV, pp. 72–81 (2021)

    Google Scholar 

  24. Zhu, X., et al.: Weakly supervised 3d semantic segmentation using cross-image consensus and inter-voxel affinity relations. In: ICCV, pp. 2834–2844 (2021)

    Google Scholar 

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This work was supported by Hong Kong Research Grants Council (RGC) Early Career Scheme grant 21207420 (CityU 9048179) and Hong Kong Research Grants Council (RGC) General Research Fund 11211221 (CityU 9043152).

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Correspondence to Yixuan Yuan .

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Guo, X., Yuan, Y. (2022). Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels. 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.

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