Skip to main content

A Novel Knowledge Distillation Technique for Colonoscopy and Medical Image Segmentation

  • Conference paper
  • First Online:
Evolution in Computational Intelligence (FICTA 2023)

Abstract

Since medical equipment has limited resources and little computational capacity, large segmentation models cannot be installed on them. Colonoscopy equipment, which has little computer power for deep learning models, is one such example where large segmentation models cannot be embedded. The solution to this problem is to perform model compression on cutting-edge models that have demonstrated outstanding diagnostic and prediction capabilities while performing segmentation. Knowledge distillation of large medical image segmentation models is the main focus of our study. However, unlike other knowledge distillation papers, we find the best student and teacher pair with varied network architecture. Even though the network architectures of our student and teacher pairs are different, the pairs still achieve better results in terms of segmentation score, pixel accuracy intersection-over-union (IoU, Jaccard Index), and dice coefficient (F1 Score). Overfitting is one of the main issues with this methodology, which we were able to minimize through depth-wise model pruning and hyperparameter tuning. Finally, we achieved the Xception-Efficientb0 pair as knowledge distillation which can outperform state-of-the-art models’ values of performance metrics on Kvasir-SEG and CVC-ClinicDB datasets.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wang, R., et al.: Medical image segmentation using deep learning: a survey. IET Image Process. 16(5), 1243–1267 (2022)

    Article  Google Scholar 

  2. Roth, H.R., Oda, H., Zhou, X., Shimizu, N., Yang, Y., Hayashi, Y., et al.: An application of cascaded 3D fully convolutional networks for medical image segmentation. Comput. Med. Imag. Graph. 66, 90–99 (2018)

    Article  Google Scholar 

  3. Masud, M., et al.: A machine learning approach to diagnosing lung and colon cancer using a deep learning-based classification framework. Sensors 21(3), 748 (2021)

    Article  Google Scholar 

  4. Brenner, H., Stock, C., Hoffmeister, M.: Effect of screening sigmoidoscopy and screening colonoscopy on colorectal cancer incidence and mortality: systematic review and meta-analysis of randomised controlled trials and observational studies. BMJ 128, 348 (2014)

    Google Scholar 

  5. Asano, T.K., McLeod, R.S.: Dietary fibre for the prevention of colorectal adenomas and carcinomas. Cochrane Database Syst. Rev. 1, CD003430 (2002)

    Google Scholar 

  6. Sivananthan, A., Glover, B., Ayaru, L., Patel, K., Darzi, A., Patel, N.: The evolution of lower gastrointestinal endoscopy: where are we now? Therap. Adv. Gastrointest. Endosc. 13, 2631774520979591 (2020)

    Article  Google Scholar 

  7. Eu, C.Y., Tang, T.B., Lin, C.H., Lee, L.H., Lu, C.K.: Automatic polyp segmentation in colonoscopy images using a modified deep convolutional encoder-decoder architecture. Sensors 21(16), 5630 (2021)

    Article  Google Scholar 

  8. Brandao, P.: Enhancing endoscopic navigation and polyp detection using artificial intelligence (Doctoral dissertation, UCL (University College London)) (2021)

    Google Scholar 

  9. Zhang, J., Tao, D.: Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things. IEEE Internet Things J. 8(10), 7789–7817 (2020)

    Article  MathSciNet  Google Scholar 

  10. Feng, J., Li, S., Li, X., Wu, F., Tian, Q., Yang, M.H., Ling, H.: Taplab: a fast framework for semantic video segmentation tapping into compressed-domain knowledge. IEEE Trans. Pattern Anal. Mach. Intell. 44(3), 1591–1603 (2020)

    Article  Google Scholar 

  11. Wang, W., Zhou, T., Porikli, F., Crandall, D., Van Gool, L.: A survey on deep learning techniques for video segmentation. arXiv preprint arXiv:2107.01153 (2021)

  12. Xie, J., Shuai, B., Hu, J.F., Lin, J., Zheng, W.S.: Improving fast segmentation with teacher-student learning. arXiv preprint arXiv:1810.08476 (2018)

  13. Li, G., Yun, I., Kim, J., Kim, J.: Dabnet: depth-wise asymmetric bottleneck for real-time semantic segmentation. arXiv preprint arXiv:1907.11357 (2019)

  14. Sanchez-Peralta, L.F., Bote-Curiel, L., Picon, A., Sanchez-Margallo, F.M., Pagador, J.B.: Deep learning to find colorectal polyps in colonoscopy: a systematic literature review. Artif. Intell. Med. 108, 101923 (2020)

    Article  Google Scholar 

  15. Kayes, M.I.: A lightweight and robust convolutional neural network for carcinogenic polyp identification (Doctoral dissertation, University of Science and Technology) (2021)

    Google Scholar 

  16. Chavarrias-Solano, P.E., Teevno, M.A., Ochoa-Ruiz, G., Ali, S.: Knowledge distillation with a class-aware loss for endoscopic disease detection. In: MICCAI Workshop on Cancer Prevention Through Early Detection, pp. 67–76. Springer, Cham (2022)

    Google Scholar 

  17. Sivaprakasam, M.: XP-Net: An Attention Segmentation Network by Dual Teacher Hierarchical Knowledge Distillation for Polyp Generalization (2022)

    Google Scholar 

  18. Kang, J., Gwak, J.: KD-ResUNet++: automatic polyp segmentation via self-knowledge distillation. In: MediaEval (2020)

    Google Scholar 

  19. Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., Lange, T.D., Johansen, D., Johansen, H.D.: Kvasir-seg: a segmented polyp dataset. In: International Conference on Multimedia Modeling, pp. 451–462. Springer, Cham (2020)

    Google Scholar 

  20. Chlap, P., et al.: A review of medical image data augmentation techniques for deep learning applications. J. Med. Imag. Radiat. Oncol. 65(5), 545–563 (2021)

    Article  Google Scholar 

  21. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019)

    Article  Google Scholar 

  22. Patel, K., Bur, A.M., Wang, G.: Enhanced U-Net: a feature enhancement network for polyp segmentation. In: Proceedings of the 2021 18th Conference on Robots and Vision (CRV), pp. 181–188. IEEE (2021)

    Google Scholar 

  23. Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: a nested U-Net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3–11. Springer, Cham (2018)

    Google Scholar 

  24. Cahall, D.E., Rasool, G., Bouaynaya, N.C., Fathallah-Shaykh, H.M.: Inception modules enhance brain tumor segmentation. Front. Comput. Neurosci. 13, 44 (2019)

    Article  Google Scholar 

  25. Chahal, E.S., Patel, A., Gupta, A., Purwar, A.: Unet based exception model for prostate cancer segmentation from MRI images. Multimedia Tools Appl. 81(26), 37333–37349 (2022)

    Article  Google Scholar 

  26. Tan, M., Le, Q.: Efficient net: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sudipta Mukhopadhyay .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kar, I., Mukhopadhyay, S., Balaiwar, R., Khule, T. (2023). A Novel Knowledge Distillation Technique for Colonoscopy and Medical Image Segmentation. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_7

Download citation

Publish with us

Policies and ethics