Abstract
Image recognition techniques heavily rely on abundant labeled data, particularly in medical contexts. Addressing the challenges associated with obtaining labeled data has led to the prominence of self-supervised learning and semi-supervised learning, especially in scenarios with limited annotated data. In this paper, we proposed an innovative approach by integrating self-supervised learning into semi-supervised models to enhance medical image recognition. Our methodology commences with pre-training on unlabeled data utilizing the BYOL method. Subsequently, we merge pseudo-labeled and labeled datasets to construct a neural network classifier, refining it through iterative fine-tuning. Experimental results on three different datasets demonstrate that our approach optimally leverages unlabeled data, outperforming existing methods in terms of accuracy for medical image recognition.
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
Similar content being viewed by others
References
Rajkomar, A., Dean, J., Kohane, I.: Machine learning in medicine. New Engl. J. Med. 380, 1347–1358 (2019). https://doi.org/10.1056/NEJMra1814259
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017). https://doi.org/10.1016/j.media.2017.07.005
Chaturvedi, K., Braytee, A., Li, J., Prasad, M.: SS-CPGAN: self-supervised cut-and-pasting generative adversarial network for object segmentation. Sensors 23(7), 3649 (2023)
Grill, J.B., et al.: Bootstrap your own latent: a new approach to self-supervised learning. arXiv.org (2020)
Zhou, Y., et al.: Vgg-fusionnet: a feature fusion framework from ct scan and chest x-ray images based deep learning for covid-19 detection. In: 2022 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1–9. IEEE (2022)
Esteva, A.: Dermatologist-level classification of skin cancer with deep neural networks. Nature (London) 542(7639), 115–118 (2017). https://doi.org/10.1038/nature21056
Filipovych, R., Davatzikos, C.: Semi-supervised pattern classification of medical images: application to mild cognitive impairment (mci). NeuroImage (Orlando, Fla.) 55(3), 1109–1119 (2011). https://doi.org/10.1016/j.neuroimage.2010.12.066
Peng, Z., et al.: Faxmatch: multi-curriculum pseudo-labeling for semi-supervised medical image classification. Med. Phys. (Lancaster) 50(5), 3210–3222 (2023). https://doi.org/10.1002/mp.16312
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). https://doi.org/10.1109/TMI.2020.2995518
Zhao, H., et al.: Anomaly detection for medical images using self-supervised and translation-consistent features. IEEE Trans. Med. Imaging 40(4), 1404–1415 (2021). https://doi.org/10.1109/TMI.2020.3000458
Kermany, D., Zhang, K., Goldbaum, M.: Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley Data (2018). https://doi.org/10.17632/rscbjbr9sj.2
Sahoo, P., Roy, I., Ahlawat, R., et al.: Potential diagnosis of covid-19 from chest x-ray and ct findings using semi-supervised learning. Phys. Eng. Sci. Med. 45(1), 31–42 (2022). https://doi.org/10.1007/s13246-021-01075-2
Liu, P., Qian, W., Cao, J., Xu, D.: Semi-supervised medical image classification via increasing prediction diversity. Appl. Intell. (Dordrecht, Netherlands) 53(9), 10162–10175 (2023). https://doi.org/10.1007/s10489-022-04012-2
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. Adv. Neural Inf. Process. Syst. 30 (2017)
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: Mixmatch: a holistic approach to semi-supervised learning. Adv. Neural Inf. Process. Syst. 32 (2019)
Sohn, K., et al.: Fixmatch: simplifying semi-supervised learning with consistency and confidence. Adv. Neural. Inf. Process. Syst. 33, 596–608 (2020)
Miyato, T., Maeda, S.I., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979–1993 (2018)
Cui, S., Wang, S., Zhuo, J., Li, L., Huang, Q., Tian, Q.: Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3941–3950 (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
Wang, X., Chen, H., Xiang, H., Lin, H., Lin, X., Heng, P.A.: Deep virtual adversarial self-training with consistency regularization for semi-supervised medical image classification. Med. Image Anal. 70, 102010 (2021)
Liu, P., Qian, W., Cao, J., Xu, D.: Semi-supervised medical image classification via increasing prediction diversity. Appl. Intell. 53(9), 10162–10175 (2023)
Acknowledgement
We acknowledge Kexin Liu, Qian Deng, Ruyi Jin, Shuting Li, and Yongxin Dai for their invaluable contributions to the project.
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 Switzerland AG
About this paper
Cite this paper
Feng, H., Jia, Y., Xu, R., Prasad, M., Anaissi, A., Braytee, A. (2024). Integration of Self-supervised BYOL in Semi-supervised Medical Image Recognition. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14835. Springer, Cham. https://doi.org/10.1007/978-3-031-63772-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-63772-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-63771-1
Online ISBN: 978-3-031-63772-8
eBook Packages: Computer ScienceComputer Science (R0)