Abstract
Semi-supervised learning is a promising approach for medical image segmentation with limited labeled data. Though existing consistency learning based SSL methods achieve convincing results, they neglect the finer-grained information. In this work, we propose a novel dual consistency learning (DCL) method based on characteristics of medical images for semi-supervised lung tumor segmentation. For patch shuffle consistency learning, image patches are shuffled as a strong-augmented view to improve both the student and teacher models in the mean teacher framework. For pixel contrast consistency learning, we construct a memory bank by high-quality pixel features updated with reservation and obtain anchors from wrongly classified tumor and high-confident background features, making the pixel-level feature space more discriminative. Experiments on three lung tumor datasets demonstrate the effectiveness of our method for semi-supervised medical image segmentation.
C. Cai and J. He are co-first authors who have contributed equally to this work.
This work was supported partially by the NSFC (No. 62276281, No. 61906218) and the Science and Technology Program of Guangzhou (No. 202002030371).
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
Alonso, I., et al.: Semi-supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank. In: ICCV, pp. 8199–8208 (2021)
Basak, H., et al.: An exceedingly simple consistency regularization method for semi-supervised medical image segmentation. In: ISBI, pp. 1–4 (2022)
Charles, F., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)
Chen, X., et al.: Semi-supervised semantic segmentation with cross pseudo supervision. In: CVPR, pp. 2613–2622 (2021)
DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)
Gaurav, F., et al.: Extreme consistency: overcoming annotation scarcity and domain shifts. In: MICCAI, pp. 699–709 (2020)
Geoffrey, F., et al.: Semi-supervised semantic segmentation needs strong, varied perturbations. In: BMVC (2020)
He, K., et al.: Momentum contrast for unsupervised visual representation learning. In: CVPR, pp. 9726–9735 (2020)
Isensee, F., et al.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Kihyuk, S., et al.: Fixmatch: Simplifying semi-supervised learning with consistency and confidence. In: NeurIPS. pp. 596–608 (2020)
Lei, T., et al.: Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network. TMI 42(5), 1265–1277 (2023)
Lequan, Y., et al.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: MICCAI, pp. 605–613 (2019)
Li, S., Zhang, C., He, X.: Shape-aware semi-supervised 3D semantic segmentation for medical images. In: MICCAI, pp. 552–561 (2020)
Luo, X., et al.: Semi-supervised medical image segmentation through dual-task consistency. In: AAAI, pp. 8801–8809 (2021)
Milletari, F., Navab, N., Ahmadi, S.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV, pp. 565–571 (2016)
Olaf, R., et al.: U-Net: convolutional networks for biomedical image segmentation. In: MICCAI, pp. 234–241 (2015)
Ouali, Y., et al.: Semi-supervised semantic segmentation with cross-consistency training. In: CVPR, pp. 12671–12681 (2020)
Qiao, P., et al.: Semi-supervised CT lesion segmentation using uncertainty-based data pairing and SwapMix. IEEE Trans. Med. Imaging 42(5), 1546–1562 (2023)
Simpson, A.L., Antonelli, M., Bakas, S., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)
Takeru, M., et al.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979–1993 (2019)
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: NeurIPS, pp. 1195–1204 (2017)
Vikas, V., et al.: Interpolation consistency training for semi-supervised learning. Neural Netw. 145, 90–106 (2022)
Wang, Y., et al.: Semi-supervised semantic segmentation using unreliable pseudo labels. In: CVPR (2022)
Wenguan, W., et al.: Exploring cross-image pixel contrast for semantic segmentation. In: ICCV, pp. 7283–7293 (2021)
Xiangde, L., et al.: Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. In: MICCAI (2021)
Xin, L., et al.: Semi-supervised semantic segmentation with directional context-aware consistency. In: CVPR, pp. 1205–1214 (2021)
Yang, L., et al.: St++: make self-trainingwork better for semi-supervised semantic segmentation. In: CVPR, pp. 4258–4267 (2022)
Yicheng, W., et al.: Mutual consistency learning for semi-supervised medical image segmentation. Media 81, 102530 (2022)
Yuan, J., et al.: A simple baseline for semi-supervised semantic segmentation with strong data augmentation. In: ICCV, pp. 8209–8218 (2021)
Yun, S., et al.: CutMix: regularization strategy to train strong classifiers with localizable features. In: ICCV, pp. 6022–6031 (2019)
Zenggui, C., Zhouhui, L.: Semi-supervised semantic segmentation via prototypical contrastive learning. In: ACMMM, pp. 6696–6705 (2022)
Zhong, Y., et al.: Pixel contrastive-consistent semi-supervised semantic segmentation. In: ICCV, pp. 7253–7262 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Cai, C., He, J., Zhang, M., Hu, Y., Li, Q., Ma, A.J. (2024). Patch Shuffle and Pixel Contrast: Dual Consistency Learning for Semi-supervised Lung Tumor Segmentation. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14429. Springer, Singapore. https://doi.org/10.1007/978-981-99-8469-5_40
Download citation
DOI: https://doi.org/10.1007/978-981-99-8469-5_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8468-8
Online ISBN: 978-981-99-8469-5
eBook Packages: Computer ScienceComputer Science (R0)