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CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge Distillation (2405.00354v2)

Published 1 May 2024 in cs.CV

Abstract: Semi-supervised learning for medical image segmentation presents a unique challenge of efficiently using limited labeled data while leveraging abundant unlabeled data. Despite advancements, existing methods often do not fully exploit the potential of the unlabeled data for enhancing model robustness and accuracy. In this paper, we introduce CrossMatch, a novel framework that integrates knowledge distillation with dual perturbation strategies-image-level and feature-level-to improve the model's learning from both labeled and unlabeled data. CrossMatch employs multiple encoders and decoders to generate diverse data streams, which undergo self-knowledge distillation to enhance consistency and reliability of predictions across varied perturbations. Our method significantly surpasses other state-of-the-art techniques in standard benchmarks by effectively minimizing the gap between training on labeled and unlabeled data and improving edge accuracy and generalization in medical image segmentation. The efficacy of CrossMatch is demonstrated through extensive experimental validations, showing remarkable performance improvements without increasing computational costs. Code for this implementation is made available at https://github.com/AiEson/CrossMatch.git.

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References (38)
  1. K. Han, V. S. Sheng, Y. Song, Y. Liu, C. Qiu, S. Ma, and Z. Liu, “Deep semi-supervised learning for medical image segmentation: A review,” Expert Systems with Applications, p. 123052, 2024.
  2. N. Souly, C. Spampinato, and M. Shah, “Semi supervised semantic segmentation using generative adversarial network,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 5688–5696.
  3. S. Mittal, M. Tatarchenko, and T. Brox, “Semi-supervised semantic segmentation with high-and low-level consistency,” IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 4, pp. 1369–1379, 2019.
  4. X. Chen, Y. Yuan, G. Zeng, and J. Wang, “Semi-supervised semantic segmentation with cross pseudo supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 2613–2622.
  5. H. Hu, F. Wei, H. Hu, Q. Ye, J. Cui, and L. Wang, “Semi-supervised semantic segmentation via adaptive equalization learning,” Advances in Neural Information Processing Systems, vol. 34, pp. 22 106–22 118, 2021.
  6. K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. D. Cubuk, A. Kurakin, and C.-L. Li, “Fixmatch: Simplifying semi-supervised learning with consistency and confidence,” Advances in neural information processing systems, vol. 33, pp. 596–608, 2020.
  7. L. Yang, W. Zhuo, L. Qi, Y. Shi, and Y. Gao, “St++: Make self-training work better for semi-supervised semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 4268–4277.
  8. L. Yang, L. Qi, L. Feng, W. Zhang, and Y. Shi, “Revisiting weak-to-strong consistency in semi-supervised semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 7236–7246.
  9. S. Gao, Z. Zhang, J. Ma, Z. Li, and S. Zhang, “Correlation-aware mutual learning for semi-supervised medical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention.   Springer, 2023, pp. 98–108.
  10. Y. Zhang, T. Xiang, T. M. Hospedales, and H. Lu, “Deep mutual learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4320–4328.
  11. Y. Wu, M. Xu, Z. Ge, J. Cai, and L. Zhang, “Semi-supervised left atrium segmentation with mutual consistency training,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II 24.   Springer, 2021, pp. 297–306.
  12. Y. Wu, Z. Ge, D. Zhang, M. Xu, L. Zhang, Y. Xia, and J. Cai, “Mutual consistency learning for semi-supervised medical image segmentation,” Medical Image Analysis, vol. 81, p. 102530, 2022.
  13. Y. Xie, Y. Yin, Q. Li, and Y. Wang, “Deep mutual distillation for semi-supervised medical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention.   Springer, 2023, pp. 540–550.
  14. S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo, “Cutmix: Regularization strategy to train strong classifiers with localizable features,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 6023–6032.
  15. H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,” arXiv preprint arXiv:1710.09412, 2017.
  16. Y. Liu, Y. Tian, Y. Chen, F. Liu, V. Belagiannis, and G. Carneiro, “Perturbed and strict mean teachers for semi-supervised semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 4258–4267.
  17. G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531, 2015.
  18. S. Yun, J. Park, K. Lee, and J. Shin, “Regularizing class-wise predictions via self-knowledge distillation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 13 876–13 885.
  19. L. Zhang, J. Song, A. Gao, J. Chen, C. Bao, and K. Ma, “Be your own teacher: Improve the performance of convolutional neural networks via self distillation,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 3713–3722.
  20. Y. Grandvalet and Y. Bengio, “Semi-supervised learning by entropy minimization,” Advances in neural information processing systems, vol. 17, 2004.
  21. H. Pham, Z. Dai, Q. Xie, and Q. V. Le, “Meta pseudo labels,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 11 557–11 568.
  22. Y. Zou, Z. Zhang, H. Zhang, C.-L. Li, X. Bian, J.-B. Huang, and T. Pfister, “Pseudoseg: Designing pseudo labels for semantic segmentation,” arXiv preprint arXiv:2010.09713, 2020.
  23. Y. Wang, H. Wang, Y. Shen, J. Fei, W. Li, G. Jin, L. Wu, R. Zhao, and X. Le, “Semi-supervised semantic segmentation using unreliable pseudo-labels,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 4248–4257.
  24. D. Berthelot, N. Carlini, E. D. Cubuk, A. Kurakin, K. Sohn, H. Zhang, and C. Raffel, “Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring,” arXiv preprint arXiv:1911.09785, 2019.
  25. H. Zhang, Z. Zhang, A. Odena, and H. Lee, “Consistency regularization for generative adversarial networks,” arXiv preprint arXiv:1910.12027, 2019.
  26. Y. Wang, H. Chen, Q. Heng, W. Hou, Y. Fan, Z. Wu, J. Wang, M. Savvides, T. Shinozaki, B. Raj et al., “Freematch: Self-adaptive thresholding for semi-supervised learning,” arXiv preprint arXiv:2205.07246, 2022.
  27. L. Yu, S. Wang, X. Li, C.-W. Fu, and P.-A. Heng, “Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II 22.   Springer, 2019, pp. 605–613.
  28. Y. Bai, D. Chen, Q. Li, W. Shen, and Y. Wang, “Bidirectional copy-paste for semi-supervised medical image segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 11 514–11 524.
  29. A. Tarvainen and H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,” Advances in neural information processing systems, vol. 30, 2017.
  30. H. Xiao, D. Li, H. Xu, S. Fu, D. Yan, K. Song, and C. Peng, “Semi-supervised semantic segmentation with cross teacher training,” Neurocomputing, vol. 508, pp. 36–46, 2022.
  31. X. Luo, J. Chen, T. Song, and G. Wang, “Semi-supervised medical image segmentation through dual-task consistency,” in Proceedings of the AAAI conference on artificial intelligence, vol. 35, no. 10, 2021, pp. 8801–8809.
  32. S. Li, C. Zhang, and X. He, “Shape-aware semi-supervised 3d semantic segmentation for medical images,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23.   Springer, 2020, pp. 552–561.
  33. X. Luo, G. Wang, W. Liao, J. Chen, T. Song, Y. Chen, S. Zhang, D. N. Metaxas, and S. Zhang, “Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency,” Medical Image Analysis, vol. 80, p. 102517, 2022.
  34. Y. Wu, Z. Wu, Q. Wu, Z. Ge, and J. Cai, “Exploring smoothness and class-separation for semi-supervised medical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention.   Springer, 2022, pp. 34–43.
  35. J. Yuan, Y. Liu, C. Shen, Z. Wang, and H. Li, “A simple baseline for semi-supervised semantic segmentation with strong data augmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 8229–8238.
  36. S. Liu, S. Zhi, E. Johns, and A. J. Davison, “Bootstrapping semantic segmentation with regional contrast,” arXiv preprint arXiv:2104.04465, 2021.
  37. I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101, 2017.
  38. G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter, “Self-normalizing neural networks,” Advances in neural information processing systems, vol. 30, 2017.
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