Skip to main content
SpringerLink
Log in

A semi-supervised medical image classification method based on combined pseudo-labeling and distance metric consistency

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In medical image analysis, obtaining high-quality labeled data is expensive, and there is a large amount of unlabeled image data that is not utilized. Semi-supervised learning can use unlabeled data to improve the model performance in the presence of data scarcity medical image analysis. In this paper, we propose a semi-supervised framework for medical image classification considering both feature extraction layer information and semantic classification layer information. It is a method that includes a combined pseudo-labeling strategy and a feature distance metric consistency method. Compared with other semi-supervised classification methods, our method significantly improves the accuracy of pseudo-labels by combining the feature metric pseudo-label with the semantic classification pseudo-label and enables the model to explore deeper information by constraining the distance relations of sample features in the feature space. We conducted extensive experiments on two public medical image datasets to demonstrate that our method outperforms various state-of-the-art semi-supervised learning methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data Availibility

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Battaglia P, Hamrick J, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro A, Faulkner R, Pascanu R (2018) Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261

  2. Berthelot D, Carlini N, Cubuk E, Kurakin A, Sohn K, Zhang H, Raffel C (2019) Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring. arXiv:1911.09785

  3. Berthelot D, Carlini N, Goodfellow I, Papernot N, Oliver A, Raffel C (2019) Mixmatch: A holistic approach to semi-supervised learning. Adv in Neural inf Process syst 32

  4. Chapelle O, Chi M, Zien A (2006) A continuation method for semi-supervised SVMs. In Proceedings of the 23rd international conference on Machine learning pp. 185-192

  5. Cohen J, Morrison P, Dao L (2020) COVID-19 image data collection. arXiv:2003.11597

  6. Girshick R (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision pp. 1440–1448

  7. Grandvalet Y, Bengio Y (2004) Semi-supervised learning by entropy minimization. Advances in neural information processing systems, 17

  8. Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) Vol. 2, pp. 1735–1742. IEEE

  9. Huang G, Liu Z, Van Der Maaten L, Weinberger K (2017) Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition pp. 4700–4708

  10. Iscen A, Tolias G, Avrithis Y, Chum O (2019) Label propagation for deep semi-supervised learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 5070–5079

  11. Kim E, Kim S, Seo M, Yoon S (2021) XProtoNet: diagnosis in chest radiography with global and local explanations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 15719-15728)

  12. Krizhevsky A, Sutskever I, Hinton G (2017) Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6):84–90

    Article  Google Scholar 

  13. Laine S, Aila T (2016) Temporal ensembling for semi-supervised learning. arXiv:1610.02242

  14. Lecouat B, Chang K, Foo C, Unnikrishnan B, Brown J, Zenati H,Beers A, Chandrasekhar V, Kalpathy-Cramer J, Krishnaswamy P (2018) Semi-supervised deep learning for abnormality classification in retinal images. arXiv:1812.07832

  15. Lee D (2013) Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML 3(2):896

    Google Scholar 

  16. Liu Q, Yu L, Luo L, Dou Q, Heng P (2020) Semi-supervised medical image classification with relation-driven self-ensembling model. IEEE Transactions on Medical Imaging 39(11):3429–3440

    Article  PubMed  Google Scholar 

  17. Liu Y, Cao J, Li B, Yuan C, Hu W, Li Y, Duan Y (2019) Knowledge distillation via instance relationship graph. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 7096—7104

  18. Liu F, Tian Yu, Chen Y, Liu Y, Belagiannis V, Carneiro G (2022) ACPL: Anti-Curriculum Pseudo-Labelling for Semi-Supervised Medical Image Classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 20697–20706

  19. Lu H, An C, Zheng E, Lu Y (2014) Dissimilarity based ensemble of extreme learning machine for gene expression data classification. Neurocomputing 128:22–30

    Article  Google Scholar 

  20. Lu H, Chen J, Yan K, Jin Q, Xue Y, Gao Z (2017) A hybrid feature selection algorithm for gene expression data classification. Neurocomputing 256:56–62

    Article  Google Scholar 

  21. Lu H, Yang L, Yan K, Xue Y, Gao Z (2017) A cost-sensitive rotation forest algorithm for gene expression data classification. Neurocomputing 228:270–276

    Article  Google Scholar 

  22. Madani A, Ong J, Tibrewal A, Mofrad M (2018) Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease. NPJ digital medicine 1(1):1–11

    Article  Google Scholar 

  23. Madani A, Moradi M, Karargyris A, Syeda-Mahmood T (2018) Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation. In 2018 IEEE 15th International symposium on biomedical imaging (ISBI 2018) pp. 1038–1042. IEEE

  24. Mangal A, Kalia S, Rajgopal H, Rangarajan K, Namboodiri V, Banerjee S, Arora C (2020) CovidAID: COVID-19 detection using chest X-ray. arXiv:2004.09803

  25. Miyato T, Maeda S, Koyama M, Ishii S (2018) Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE transactions on pattern analysis and machine intelligence 41(8):1979–1993

    Article  PubMed  Google Scholar 

  26. Mooney P (2018) Kaggle chest x-ray images (pneumonia) dataset. https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

  27. Prakash V, Nithya D (2014) A survey on semi-supervised learning techniques. arXiv:1402.4645

  28. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv:1711.05225

  29. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg A, Fei-Fei L (2015) Imagenet large scale visual recognition challenge. International journal of computer vision 115(3):211–252

    Article  MathSciNet  Google Scholar 

  30. Sajjadi M, Javanmardi M, Tasdizen T (2016) Regularization with stochastic transformations and perturbations for deep semi-supervised learning. Adv Neural Inf Process Syst 29

  31. Sohn K, Berthelot D, Carlini N, Zhang Z, Zhang H, Raffel C, Cubuk E, Kurakin A, Li C (2020) Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Advances in neural information processing systems 33:596–608

    Google Scholar 

  32. Taherkhani F, Dabouei A, Soleymani S, Dawson J, Nasrabadi N (2021) Self-supervised wasserstein pseudo-labeling for semi-supervised image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 12267-12277

  33. Tarvainen A, Valpola H (2017) Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Adv Neural inf Process syst 30

  34. Tschandl P, Rosendahl C, Kittler H (2018) The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data 5(1):1–9

    Article  Google Scholar 

  35. Van Engelen J, Hoos H (2020) A survey on semi-supervised learning. Machine Learning 109(2):373–440

    Article  MathSciNet  Google Scholar 

  36. Wang D, Zhang Y, Zhang K, Wang L (2020) Focalmix: Semi-supervised learning for 3d medical image detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 3951–3960

  37. Yi X, Walia E, Babyn P (2018) Unsupervised and semi-supervised learning with categorical generative adversarial networks assisted by wasserstein distance for dermoscopy image classification. arXiv:1804.03700

  38. Zhang B, Wang Y, Hou W, Wu H, Wang J, Okumura M, Shinozaki T (2021) Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling. Adv Neural Inf Process Syst 34:18408–18419

    Google Scholar 

  39. Zhang W, Zhu L, Hallinan J, Zhang S, Makmur A, Cai Q, Ooi B (2022) Boostmis: Boosting medical image semi-supervised learning with adaptive pseudo labeling and informative active annotation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 20666–20676

  40. Zheng M, You S, Huang L, Wang F, Qian C, Xu C (2022) SimMatch: Semi-supervised Learning with Similarity Matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 14471–14481

  41. Zhou Y, He X, Huang L, Liu L, Zhu F, Cui S, Shao L (2019) Collaborative learning of semi-supervised segmentation and classification for medical images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition pp. 2079-2088

  42. Zhou H, Wang C, Li H, Wang G, Zhang S, Li W, Yu Y (2021) SSMD: semi-supervised medical image detection with adaptive consistency and heterogeneous perturbation. Med Image Anal 72 102117

Download references

Acknowledgements

In this paper, the research is sponsored by the National Natural Science Foundation of China (61272315), the Natural Science Foundation of Zhejiang Province (LQ20F030015, LY21F020028, 2023C01040), and the Foundation of Zhejiang Educational Committee (Y202147815).

Author information

Authors and Affiliations

  1. China Jiliang University, Hangzhou, Zhejiang, 310018, China

    Boya Ke, Huijuan Lu, Cunqian You & Wenjie Zhu

  2. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, Hangzhou, Zhejiang, 310018, China

    Boya Ke, Huijuan Lu, Cunqian You & Wenjie Zhu

  3. Drore Information and Technology Co., Hangzhou, Zhejiang, 310018, China

    Huijuan Lu

  4. College of Biomedical Engineering and Instrumentation Science Zhejiang University, Hangzhou, Zhejiang, 310018, China

    Li Xie

  5. Institute of Advanced Digital Technologies and Instrumentation Zhejiang Province, Hangzhou, Zhejiang, 310018, China

    Li Xie

  6. Department of Electrical and Computer Engineering Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Yudong Yao

Authors
  1. Boya Ke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huijuan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cunqian You
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wenjie Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Li Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yudong Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Huijuan Lu or Cunqian You.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ke, B., Lu, H., You, C. et al. A semi-supervised medical image classification method based on combined pseudo-labeling and distance metric consistency. Multimed Tools Appl 83, 33313–33331 (2024). https://doi.org/10.1007/s11042-023-16383-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16383-w

Keywords

Search

Navigation