Abstract
Self-ensembling framework has proven to be a powerful paradigm for semi-supervised medical image classification by leveraging abundant unlabeled data. However, the unlabeled data used in most of self-ensembling methods are equally weighted, which adversely affects the classification performance of models when difference exists among unlabeled data acquired from different populations, equipment and environments. To address this issue, we propose a novel reliability-aware contrastive self-ensembling framework, which can leverage the reliable unlabeled data selectively. Concretely, we introduce a weight function to the mean teacher paradigm for mapping the probability predictions of unlabeled data to corresponding weights that reflect their reliability. Hence, we can safely leverage the predictions of related unlabeled data under different perturbations to construct a reliable consistency loss. Besides, we further design a novel reliable contrastive loss to achieve better intra-class compactness and inter-class separability for the normalized embeddings derived from related unlabeled data. As a result, our reliability-aware scheme enables the contrastive self-ensembling framework concurrently capture both the reliable data-level and data-structure-level information, thereby improving the robustness and generalization power of the model. Experiments on two publicly available medical image datasets demonstrate the superiority of the proposed method. Our model is available at https://github.com/Mwnic/RAC-MT.
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
Bard, J.F.: Practical Bilevel Optimization: Algorithms and Applications, vol. 30. Springer, Dordrecht (2013). https://doi.org/10.1007/978-1-4757-2836-1
Guo, L.Z., Zhang, Z.Y., Jiang, Y., Li, Y.F., Zhou, Z.H.: Safe deep semi-supervised learning for unseen-class unlabeled data. In: International Conference on Machine Learning, pp. 3897–3906. PMLR (2020)
Gyawali, P.K., Ghimire, S., Bajracharya, P., Li, Z., Wang, L.: Semi-supervised medical image classification with global latent mixing. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 604–613. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_59
Hang, W., et al.: Local and global structure-aware entropy regularized mean teacher model for 3D left atrium segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 562–571. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_55
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Khosla, P., et al.: Supervised contrastive learning. Adv. Neural Inf. Process. Syst. 33, 18661–18673 (2020)
Kim, B., Choo, J., Kwon, Y.D., Joe, S., Min, S., Gwon, Y.: Selfmatch: combining contrastive self-supervision and consistency for semi-supervised learning. arXiv preprint arXiv:2101.06480 (2021)
Li, X., Yu, L., Chen, H., Fu, C.W., Xing, L., Heng, P.A.: Transformation-consistent self-ensembling model for semisupervised medical image segmentation. IEEE Trans. Neural Netw. Learn. Syst. 32(2), 523–534 (2020)
Liu, F., Tian, Yu., Cordeiro, F.R., Belagiannis, V., Reid, I., Carneiro, G.: Self-supervised mean teacher for semi-supervised chest X-Ray classification. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds.) MLMI 2021. LNCS, vol. 12966, pp. 426–436. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87589-3_44
Liu, Q., Yang, H., Dou, Q., Heng, P.-A.: Federated semi-supervised medical image classification via inter-client relation matching. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 325–335. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87199-4_31
Liu, Q., Yu, L., Luo, L., Dou, Q., Heng, P.A.: Semi-supervised medical image classification with relation-driven self-ensembling model. IEEE Trans. Med. Imaging 39(11), 3429–3440 (2020)
Liu, R., Gao, J., Zhang, J., Meng, D., Lin, Z.: Investigating bi-level optimization for learning and vision from a unified perspective: a survey and beyond. IEEE Trans. Pattern Anal. Mach. Intell. (2021). https://doi.org/10.1109/TPAMI.2021.3132674
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Samuli, L., Timo, A.: Temporal ensembling for semi-supervised learning. In: International Conference on Learning Representations, vol. 4 (2017)
Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016)
Su, H., Shi, X., Cai, J., Yang, L.: Local and global consistency regularized mean teacher for semi-supervised nuclei classification. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 559–567. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_62
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, pp. 1195–1204 (2017)
Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1–9 (2018)
van Engelen, J.E., Hoos, H.H.: A survey on semi-supervised learning. Mach. Learn. 109(2), 373–440 (2019). https://doi.org/10.1007/s10994-019-05855-6
Wang, R., Wu, Y., Chen, H., Wang, L., Meng, D.: Neighbor matching for semi-supervised learning. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 439–449. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_41
Wang, Z., Liu, Q., Dou, Q.: Contrastive cross-site learning with redesigned net for covid-19 CT classification. IEEE J. Biomed. Health Inf. 24(10), 2806–2813 (2020)
Yang, P., Chen, B.: Robust kullback-leibler divergence and universal hypothesis testing for continuous distributions. IEEE Trans. Inf. Theor. 65(4), 2360–2373 (2018)
Yu, L., Wang, S., Li, X., Fu, C.-W., Heng, P.-A.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 605–613. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_67
Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (61902197, 61802177), the Open Project of State Key Laboratory for Novel Software Technology at Nanjing University (KFKT2020B11), and Hong Kong Research Grants Council under General Research Fund (15218521).
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
Hang, W., Huang, Y., Liang, S., Lei, B., Choi, KS., Qin, J. (2022). Reliability-Aware Contrastive Self-ensembling for Semi-supervised Medical Image Classification. 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 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_71
Download citation
DOI: https://doi.org/10.1007/978-3-031-16431-6_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16430-9
Online ISBN: 978-3-031-16431-6
eBook Packages: Computer ScienceComputer Science (R0)
