Skip to main content

Advertisement

Springer Nature Link
Log in

Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The classification of esophageal disease based on gastroscopic images is important in the clinical treatment, and is also helpful in providing patients with follow-up treatment plans and preventing lesion deterioration. In recent years, deep learning has achieved many satisfactory results in gastroscopic image classification tasks. However, most of them need a training set that consists of large numbers of images labeled by experienced experts. To reduce the image annotation burdens and improve the classification ability on small labeled gastroscopic image datasets, this study proposed a novel semi-supervised efficient contrastive learning (SSECL) classification method for esophageal disease. First, an efficient contrastive pair generation (ECPG) module was proposed to generate efficient contrastive pairs (ECPs), which took advantage of the high similarity features of images from the same lesion. Then, an unsupervised visual feature representation containing the general feature of esophageal gastroscopic images is learned by unsupervised efficient contrastive learning (UECL). At last, the feature representation will be transferred to the down-stream esophageal disease classification task. The experimental results have demonstrated that the classification accuracy of SSECL is 92.57%, which is better than that of the other state-of-the-art semi-supervised methods and is also higher than the classification method based on transfer learning (TL) by 2.28%. Thus, SSECL has solved the challenging problem of improving the classification result on small gastroscopic image dataset by fully utilizing the unlabeled gastroscopic images and the high similarity information among images from the same lesion. It also brings new insights into medical image classification tasks.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Bray, F., Ferlay, J., Siegel, I.S.R.L., Torre, L.A. and Ahmedin Jemal. (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA CANCER J CLIN, 68(6):394–424. https://doi.org/10.3322/caac.21492

    Article  PubMed  Google Scholar 

  2. Hu, Y., Hu, C., Zhang, H., Ping, Y. and Chen, L.Q. (2010) How does the number of resected lymph nodes influence TNM staging and prognosis for esophageal carcinoma? Annals of Surgical Oncology, 17(3):784–90. https://doi.org/10.1245/s10434-009-0818-5

    Article  PubMed  Google Scholar 

  3. José, M., Arnal, D., Arenas, Á.F. and Arbeloa, Á.L. (2015) Esophageal cancer : Risk factors , screening and endoscopic treatment in Western and Eastern countries. World Journal of Gastroenterology, 21(26):7933–43. https://doi.org/10.3748/wjg.v21.i26.7933

    Article  Google Scholar 

  4. Huang, F.L. and Yu, S.J. (2018) Esophageal cancer: Risk factors, genetic association, and treatment. Asian Journal of Surgery, Elsevier Taiwan LLC. 41(3):210–5. https://doi.org/10.1016/j.asjsur.2016.10.005

    Article  Google Scholar 

  5. Mocanu, A., Bârla, R., Hoara, P. and Constantinoiu, S. (2015) Current endoscopic methods of radical therapy in early esophageal cancer. Journal of Medicine and Life p 150–6.

  6. Guo, L.J., Xiao, X., Wu, C.C., Zeng, X., Zhang, Y., Du, J. et al. (2020) Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos). Gastrointestinal Endoscopy, American Society for Gastrointestinal Endoscopy. 91(1):41–51. https://doi.org/10.1016/j.gie.2019.08.018

    Article  Google Scholar 

  7. Huang, L.M., Yang, W.J., Huang, Z.Y., Tang, C.W. and Li, J. (2020) Artificial intelligence technique in detection of early esophageal cancer. World Journal of Gastroenterology, 26(39):5959–69. https://doi.org/10.3748/wjg.v26.i39.5959

    Article  PubMed  PubMed Central  Google Scholar 

  8. Pang, X., Zhao, Z. and Weng, Y. (2021) The role and impact of deep learning methods in computer-aided diagnosis using gastrointestinal endoscopy. Diagnostics, 11(4):694.

    Article  Google Scholar 

  9. Du, W., Rao, N., Liu, D., Jiang, H., Luo, C., Li, Z. et al. (2019) Review on the applications of deep learning in the analysis of gastrointestinal endoscopy images. IEEE Access, IEEE. 7142053–69. https://doi.org/10.1109/ACCESS.2019.2944676

  10. Liu, D.Y., Gan, T., Rao, N.N., Xing, Y.W., Zheng, J., Li, S. et al. (2016) Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process. Medical Image Analysis, Elsevier B.V. 32281–94. https://doi.org/10.1016/j.media.2016.04.007

  11. Riaz, F., Silva, F.B., Ribeiro, M.D. and Coimbra, M.T. (2012) Invariant Gabor texture descriptors for classification of gastroenterology images. IEEE Transactions on Biomedical Engineering, IEEE. 59(10):2893–904. https://doi.org/10.1109/TBME.2012.2212440

    Article  Google Scholar 

  12. van der Sommen, F., Zinger, S., Schoon, E.J. and de With, P.H.N. (2014) Supportive automatic annotation of early esophageal cancer using local gabor and color features. Neurocomputing, Elsevier. 14492–106. https://doi.org/10.1016/j.neucom.2014.02.066

  13. Bernal, J., Tajkbaksh, N., Sanchez, F.J., Matuszewski, B.J., Chen, H., Yu, L. et al. (2017) Comparative validation of polyp detection methods in video colonoscopy: results from the miccai 2015 endoscopic vision challenge. IEEE Transactions on Medical Imaging, 36(6):1231–49. https://doi.org/10.1109/TMI.2017.2664042

    Article  PubMed  Google Scholar 

  14. Liu, X., Wang, C., Bai, J. and Liao, G. (2020) Fine-tuning pre-trained convolutional neural networks for gastric precancerous disease classification on magnification narrow-band imaging images. Neurocomputing, Elsevier B.V. 392253–67. https://doi.org/10.1016/j.neucom.2018.10.100

  15. Liao, J., Lam, H.K., Jia, G., Gulati, S., Bernth, J., Poliyivets, D. et al. (2021) A case study on computer-aided diagnosis of nonerosive reflux disease using deep learning techniques. Neurocomputing, Elsevier B.V. 445149–66. https://doi.org/10.1016/j.neucom.2021.02.049

  16. Struyvenberg, M.R., de Groof, A.J., van der Putten, J., van der Sommen, F., Baldaque-Silva, F., Omae, M. et al. (2021) A computer-assisted algorithm for narrow-band imaging-based tissue characterization in Barrett’s esophagus. Gastrointestinal Endoscopy, American Society for Gastrointestinal Endoscopy. 93(1):89–98. https://doi.org/10.1016/j.gie.2020.05.050

    Article  Google Scholar 

  17. Nakagawa, K., Ishihara, R., Aoyama, K., Ohmori, M., Nakahira, H., Matsuura, N. et al. (2019) Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists. Gastrointestinal Endoscopy, American Society for Gastrointestinal Endoscopy. 90(3):407–14. https://doi.org/10.1016/j.gie.2019.04.245

    Article  Google Scholar 

  18. Wang, C.C., Chiu, Y.C., Chen, W.L., Yang, T.W., Tsai, M.C. and Tseng, M.H. (2021) Article a deep learning model for classification of endoscopic gastroesophageal reflux disease. International Journal of Environmental Research and Public Health, 18(5):1–14. https://doi.org/10.3390/ijerph18052428

    Article  Google Scholar 

  19. Horie, Y., Yoshio, T., Aoyama, K., Yoshimizu, S., Horiuchi, Y., Ishiyama, A. et al. (2019) Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointestinal Endoscopy, Elsevier, Inc. 89(1):25–32. https://doi.org/10.1016/j.gie.2018.07.037

  20. Van Riel, S., Van Der Sommen, F., Zinger, S., Schoon, E.J. and De With, P.H.N. (2018) Automatic detection of early esophageal cancer with CNNS using transfer learning. Proceedings - International Conference on Image Processing, ICIP, Athens, Greece. p. 1383–7. https://doi.org/10.1109/ICIP.2018.8451771

  21. Gour, N. and Khanna, P. (2021) Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network. Biomedical Signal Processing and Control, 66102329. https://doi.org/10.1016/j.bspc.2020.102329

  22. Mahbod, A., Schaefer, G., Wang, C., Ecker, R. and Dorffner, G. (2021) Investigating and exploiting image resolution for transfer learning-based skin lesion classification. 2020 25th International Conference on Pattern Recognition (ICPR), p. 4047–53.

  23. Du, W., Rao, N., Wang, Y., Hu, D. and Yong, J. (2020) Efficient transfer learning used in the classification of gastroscopic images with small dataset. 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2020, IEEE, Chengdu. p. 73–6. https://doi.org/10.1109/ICCWAMTIP51612.2020.9317450

  24. Srinidhi, C.L., Ciga, O. and Martel, A.L. (2021) Deep neural network models for computational histopathology: A survey. Medical Image Analysis, Elsevier B.V. 67101813. https://doi.org/10.1016/j.media.2020.101813

  25. Ouali, Y., Hudelot, C. and Tami, M. (2020) An overview of deep semi-supervised learning. ArXiv: 2006.05278, 2020.

  26. He, K., Fan, H., Wu, Y., Xie, S. and Girshick, R. (2020) Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 9726–35. https://doi.org/10.1109/CVPR42600.2020.00975

  27. Grill, J.B., Strub, F., Altché, F., Tallec, C., Richemond, P.H., Buchatskaya, E. et al. (2020) Bootstrap your own latent a new approach to self-supervised learning. Advances in Neural Information Processing Systems,.

  28. Chen, T., Kornblith, S., Norouzi, M. and Hinton, G. (2020) A simple framework for contrastive learning of visual representations. 37th International Conference on Machine Learning, ICML 2020, p. 1575–85.

  29. Caron, M., Bojanowski, P., Joulin, A. and Douze, M. (2018) Deep clustering for unsupervised learning of visual features. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), p. 139–56. https://doi.org/10.1007/978-3-030-01264-9_9

  30. Liu, Q., Yu, L., Luo, L. and Heng, P.-A. (2020) Semi-supervised medical image classification with relation-driven self-ensembling. IEEE Transactions on Medical Imaging, 39(11):3429–40.

    Article  Google Scholar 

  31. Xie, Y., Zhang, J. and Xia, Y. (2019) Semi-supervised adversarial model for benign–malignant lung nodule classification on chest CT. Medical Image Analysis, Elsevier B.V. 57237–48. https://doi.org/10.1016/j.media.2019.07.004

  32. Chang, H., Han, J., Zhong, C., Snijders, A.M. and Mao, J.H. (2018) Unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(5):1182–94. https://doi.org/10.1109/TPAMI.2017.2656884

    Article  PubMed  Google Scholar 

  33. Pogorelov, K., Randel, K.R., Griwodz, C., Eskeland, S.L., De Lange, T., Johansen, D. et al. (2017) Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection. Proceedings of the 8th ACM Multimedia Systems Conference, MMSys 2017, p. 164–9. https://doi.org/10.1145/3083187.3083212

  34. Ioffe, S. and Szegedy, C. (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. 32nd International Conference on Machine Learning, ICML 2015, 1448–56.

  35. Hinton, G.E. Rectified linear units improve restricted boltzmann machines. International Conference on Machine Learning, ICML 2010, 807–14.

  36. Sun, K.H.X.Z.S.R.J. (2006) Deep residual learning for image recognition. Indian Journal of Chemistry - Section B Organic and Medicinal Chemistry, 45(8):1951–4. https://doi.org/10.1109/CVPR.2016.90

    Article  Google Scholar 

  37. He, K., Girshick, R. and Dollar, P. (2019) Rethinking imageNet pre-training. Proceedings of the IEEE International Conference on Computer Vision, p. 4917–26. https://doi.org/10.1109/ICCV.2019.00502

  38. Zoph, B., Ghiasi, G., Lin, T.-Y., Cui, Y., Liu, H., Cubuk, E.D. et al. (2020) Rethinking Pre-training and Self-training. ArXiv: 2006.06882, 2020.

  39. Van Der Maaten, L. and Hinton, G. (2008) Visualizing data using t-SNE. Journal of Machine Learning Research, 9(February):2579–625.

    Google Scholar 

  40. Du, W., Rao, N., Dong, C., Wang, Y., Hu, D., Zhu, L. et al. (2021) Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network. Biomedical Optics Express, 12(6):3066–81.

    Article  Google Scholar 

Download references

Funding

This research was supported by the National Natural Science Foundation of China (61720106004 and 61872405) and the Key R&D Project of Sichuan Province (2020YFS0243).

Author information

Authors and Affiliations

  1. Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Wenju Du, Nini Rao, Jiahao Yong, Yingchun Wang & Dingcan Hu

  2. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Wenju Du, Nini Rao, Jiahao Yong, Yingchun Wang & Dingcan Hu

  3. Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu, 610017, China

    Tao Gan & Linlin Zhu

  4. School of Information and Communication Engineering, University Electronic Science and Technology of China, Chengdu, 610054, China

    Bing Zeng

Authors
  1. Wenju Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nini Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiahao Yong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yingchun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dingcan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tao Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Linlin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Bing Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Nini Rao or Tao Gan.

Ethics declarations

Ethics approval

This article does not contain any studies with human participants performed by any of the authors.

Conflicts of interest/Competing interests

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

This article is part of the Topical Collection on Image & Signal Processing

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, W., Rao, N., Yong, J. et al. Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning. J Med Syst 46, 4 (2022). https://doi.org/10.1007/s10916-021-01782-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-021-01782-z

Keywords

Search

Navigation