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Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior

  • Max-Heinrich LavesEmail author
  • Malte Tölle
  • Tobias Ortmaier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12443)

Abstract

Uncertainty quantification in inverse medical imaging tasks with deep learning has received little attention. However, deep models trained on large data sets tend to hallucinate and create artifacts in the reconstructed output that are not anatomically present. We use a randomly initialized convolutional network as parameterization of the reconstructed image and perform gradient descent to match the observation, which is known as deep image prior. In this case, the reconstruction does not suffer from hallucinations as no prior training is performed. We extend this to a Bayesian approach with Monte Carlo dropout to quantify both aleatoric and epistemic uncertainty. The presented method is evaluated on the task of denoising different medical imaging modalities. The experimental results show that our approach yields well-calibrated uncertainty. That is, the predictive uncertainty correlates with the predictive error. This allows for reliable uncertainty estimates and can tackle the problem of hallucinations and artifacts in inverse medical imaging tasks.

Keywords

Variational inference Hallucination Deep learning 

References

  1. 1.
    Agostinelli, F., Anderson, M.R., Lee, H.: Adaptive multi-column deep neural networks with application to robust image denoising. In: Advances in Neural Information Processing Systems, pp. 1493–1501 (2013)Google Scholar
  2. 2.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Boston (2006).  https://doi.org/10.1007/978-1-4615-7566-5CrossRefzbMATHGoogle Scholar
  3. 3.
    Chang, S.G., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9), 1532–1546 (2000).  https://doi.org/10.1109/83.862633MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Cheng, Z., Gadelha, M., Maji, S., Sheldon, D.: A Bayesian perspective on the deep image prior. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5443–5451 (2019)Google Scholar
  5. 5.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. Trans. Image Process. 16(8), 2080–2095 (2007).  https://doi.org/10.1109/TIP.2007.901238MathSciNetCrossRefGoogle Scholar
  6. 6.
    Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In: ICML, pp. 1050–1059 (2016)Google Scholar
  7. 7.
    Gondara, L.: Medical image denoising using convolutional denoising autoencoders. In: International Conference on Data Mining Workshops, pp. 241–246 (2016).  https://doi.org/10.1109/ICDMW.2016.0041
  8. 8.
    Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: ICML, pp. 1321–1330 (2017)Google Scholar
  9. 9.
    van den Heuvel, T.L., de Bruijn, D., de Korte, C.L., Ginneken, B.v.: Automated measurement of fetal head circumference using 2D ultrasound images. PloS One 13(8), e0200412 (2018).  https://doi.org/10.1371/journal.pone.0200412. US dataset source
  10. 10.
    Hogg, R.V., McKean, J., Craig, A.T.: Introduction to Mathematical Statistics, 8th edn. Pearson, New York (2018)Google Scholar
  11. 11.
    Jain, V., Seung, S.: Natural image denoising with convolutional networks. In: Advances in Neural Information Processing Systems, pp. 769–776 (2009)Google Scholar
  12. 12.
    Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: NeurIPS, pp. 5574–5584 (2017)Google Scholar
  13. 13.
    Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018).  https://doi.org/10.1016/j.cell.2018.02.010CrossRefGoogle Scholar
  14. 14.
    Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: ICLR (2014)Google Scholar
  15. 15.
    Laves, M.H., Ihler, S., Fast, J.F., Kahrs, L.A., Ortmaier, T.: Well-calibrated regression uncertainty in medical imaging with deep learning. In: Medical Imaging with Deep Learning (2020)Google Scholar
  16. 16.
    Laves, M.H., Ihler, S., Kahrs, L.A., Ortmaier, T.: Semantic denoising autoencoders for retinal optical coherence tomography. In: SPIE/OSA European Conference on Biomedical Optics, vol. 11078, pp. 86–89 (2019).  https://doi.org/10.1117/12.2526936
  17. 17.
    Lee, S., Lee, M.S., Kang, M.G.: Poisson-gaussian noise analysis and estimation for low-dose x-ray images in the NSCT domain. Sensors 18(4), 1019 (2018)CrossRefGoogle Scholar
  18. 18.
    Lempitsky, V., Vedaldi, A., Ulyanov, D.: Deep Image Prior. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9446–9454 (2018).  https://doi.org/10.1109/CVPR.2018.00984
  19. 19.
    Levi, D., Gispan, L., Giladi, N., Fetaya, E.: Evaluating and calibrating uncertainty prediction in regression tasks. arXiv arXiv:1905.11659 (2019)
  20. 20.
    Li, C., Chen, C., Carlson, D., Carin, L.: Preconditioned stochastic gradient Langevin dynamics for deep neural networks. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 1788–1794 (2016)Google Scholar
  21. 21.
    Michailovich, O.V., Tannenbaum, A.: Despeckling of medical ultrasound images. Trans. Ultrason. Ferroelectr. Freq. Control 53(1), 64–78 (2006).  https://doi.org/10.1109/TUFFC.2006.1588392CrossRefGoogle Scholar
  22. 22.
    Rabbani, H., Nezafat, R., Gazor, S.: Wavelet-domain medical image denoising using bivariate Laplacian mixture model. Trans. Biomed. Eng. 56(12), 2826–2837 (2009).  https://doi.org/10.1109/TBME.2009.2028876CrossRefGoogle Scholar
  23. 23.
    Salinas, H.M., Fernandez, D.C.: Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography. IEEE Trans. Med. Imaging 26(6), 761–771 (2007).  https://doi.org/10.1109/TMI.2006.887375CrossRefGoogle Scholar
  24. 24.
    Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153–1190 (2013).  https://doi.org/10.1109/TMI.2013.2265603CrossRefGoogle Scholar
  25. 25.
    Wang, N., Tao, D., Gao, X., Li, X., Li, J.: A comprehensive survey to face hallucination. Int. J. Comput. Vis. 106(1), 9–30 (2014).  https://doi.org/10.1007/s11263-013-0645-9CrossRefGoogle Scholar
  26. 26.
    Welling, M., Teh, Y.W.: Bayesian learning via stochastic gradient Langevin dynamics. In: ICML, pp. 681–688 (2011)Google Scholar
  27. 27.
    Žabić, S., Wang, Q., Morton, T., Brown, K.M.: A low dose simulation tool for CT systems with energy integrating detectors. Med. Phys. 40(3), 031102 (2013).  https://doi.org/10.1118/1.4789628CrossRefGoogle Scholar
  28. 28.
    Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017).  https://doi.org/10.1109/TIP.2017.2662206MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Max-Heinrich Laves
    • 1
    Email author
  • Malte Tölle
    • 1
  • Tobias Ortmaier
    • 1
  1. 1.Leibniz Universität HannoverHanoverGermany

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