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2021 | OriginalPaper | Buchkapitel

Learning to Predict Error for MRI Reconstruction

verfasst von : Shi Hu, Nicola Pezzotti, Max Welling

Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

Verlag: Springer International Publishing

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Abstract

In healthcare applications, predictive uncertainty has been used to assess predictive accuracy. In this paper, we demonstrate that predictive uncertainty estimated by the current methods does not highly correlate with prediction error by decomposing the latter into random and systematic errors, and showing that the former is equivalent to the variance of the random error. In addition, we observe that current methods unnecessarily compromise performance by modifying the model and training loss to estimate the target and uncertainty jointly. We show that estimating them separately without modifications improves performance. Following this, we propose a novel method that estimates the target labels and magnitude of the prediction error in two steps. We demonstrate this method on a large-scale MRI reconstruction task, and achieve significantly better results than the state-of-the-art uncertainty estimation methods.

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Vorheriges Kapitel Orthogonal Ensemble Networks for Biomedical Image Segmentation

Nächstes Kapitel Uncertainty-Guided Progressive GANs for Medical Image Translation

Nur mit Berechtigung zugänglich

https://www.kaggle.com/c/diabetic-retinopathy-detection.

https://fastmri.org.

The original U-net code is from: https://github.com/facebookresearch/fastMRI.

The high standard deviations (SD) are due to high image noise (we note that most prior works on this dataset do not report SD, including [30]).

Adler, J., Öktem, O.: Deep Bayesian inversion. arXiv preprint arXiv:1811.05910 (2018)

Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. 2, 303–314 (1989)MathSciNetCrossRef

Davenport, T., Kalakota, R.: The potential for artificial intelligence in healthcare. Future Healthc. J. 6, 94 (2019)CrossRef

Depeweg, S., Hernández-Lobato, J.M., Doshi-Velez, F., Udluft, S.: Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning. In: ICML (2018)

Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: ICML (2016)

Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)MATH

Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICLR (2015)

Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: ICML (2017)

Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. SSS, Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7CrossRefMATH

10.

Hu, S., Worrall, D., Knegt, S., Veeling, B., Huisman, H., Welling, M.: Supervised uncertainty quantification for segmentation with multiple annotations. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 137–145. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_16CrossRef

11.

Joint Committee for Guides in Metrology: JCGM 100:2008. evaluation of measurement data - guide to the expression of uncertainty in measurement. Technical report (2008)

12.

Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: NIPS (2017)

13.

Kennedy, M.C., O’Hagan, A.: Bayesian calibration of computer models. J. Roy. Stat. Soc. Ser. B (Stat. Methodol.) 63(3), 425–464 (2001)

14.

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)

15.

Kiureghian, A.D., Ditlevsen, O.: Aleatory or epistemic? does it matter? Struct. Saf. 31, 105–112 (2009)CrossRef

16.

Kohlberger, T., Singh, V., Alvino, C., Bahlmann, C., Grady, L.: Evaluating segmentation error without ground truth. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 528–536. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_65CrossRef

17.

Kuleshov, V., Fenner, N., Ermon, S.: Accurate uncertainties for deep learning using calibrated regression. In: ICML (2018)

18.

Kwon, Y., Won, J.H., Kim, B.J., Paik, M.C.: Uncertainty quantification using Bayesian neural networks in classification: application to biomedical image segmentation. Comput. Stat. Data Anal. 142 (2019). Article no. 106816

19.

Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: NIPS (2017)

20.

Nix, D.A., Weigend, A.S.: Estimating the mean and variance of the target probability distribution. In: IEEE International Conference on Neural Networks (1994)

21.

Ovadia, Y., et al.: Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift. In: NeurIPS (2019)

22.

Pezzotti, N., et al.: An adaptive intelligence algorithm for undersampled knee MRI reconstruction. IEEE Access 8, 204825–204838 (2020)CrossRef

23.

Putzky, P., et al.: i-RIM applied to the fastMRI challenge. arXiv preprint arXiv:1910.08952 (2019)

24.

Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRef

25.

Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: CVPR (2016)

26.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. JMLR 15, 1929–1958 (2014)MathSciNetMATH

27.

Tanno, R., et al.: Bayesian image quality transfer with CNNs: exploring uncertainty in dMRI super-resolution. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 611–619. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_70CrossRef

28.

Valindria, V.V., et al.: Reverse classification accuracy: predicting segmentation performance in the absence of ground truth. IEEE Trans. Med. Imaging 36, 1597–1606 (2017)CrossRef

29.

Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)CrossRef

30.

Zbontar, J., et al.: fastMRI: an open dataset and benchmarks for accelerated MRI. arXiv preprint arXiv: 1811.08839 (2018)

Titel: Learning to Predict Error for MRI Reconstruction
verfasst von: Shi Hu
Nicola Pezzotti
Max Welling
Verlag: Springer International Publishing
Buch: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
Print ISBN: 978-3-030-87198-7

Electronic ISBN: 978-3-030-87199-4

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-87199-4_57

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