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Improving the Reliability of Semantic Segmentation of Medical Images by Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 12959)

Abstract

In this paper we propose a novel method which leverages the uncertainty measures provided by Bayesian deep networks through curriculum learning so that the uncertainty estimates are fed back to the system to resample the training data more densely in areas where uncertainty is high. We show in the concrete setting of a semantic segmentation task (iPS cell colony segmentation) that the proposed system is able to increase significantly the reliability of the model.

Keywords

  • Bayesian deep learning
  • Uncertainty
  • Curriculum learning

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References

  1. Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/. software available from tensorflow.org

  2. Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 41–48. ACM, New York (2009)

    Google Scholar 

  3. Brosch, T., Yoo, Y., Tang, L.Y.W., Li, D.K.B., Traboulsee, A., Tam, R.: Deep convolutional encoder networks for multiple sclerosis lesion segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 3–11. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_1

    CrossRef  Google Scholar 

  4. Chollet, F.: Keras (2015). https://github.com/fchollet/keras

  5. DeVries, T., Taylor, G.W.: Leveraging uncertainty estimates for predicting segmentation quality (2018)

    Google Scholar 

  6. Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017). https://doi.org/10.1038/nature21056. https://app.dimensions.ai/details/publication/pub.1074217286

    CrossRef  Google Scholar 

  7. Gal, Y.: Uncertainty in deep learning. Ph.D. thesis, University of Cambridge (2016)

    Google Scholar 

  8. Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of Machine Learning Research, vol. 48, pp. 1050–1059. PMLR, New York (2016). http://proceedings.mlr.press/v48/gal16.html

  9. Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016). https://doi.org/10.1001/jama.2016.17216

    CrossRef  Google Scholar 

  10. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014). http://arxiv.org/abs/1412.6980. cite arxiv:1412.6980 Comment: Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego (2015)

  11. Milletari, F., Navab, N., Ahmadi, S.: V-net: fully convolutional neural networks for volumetric medical image segmentation. CoRR abs/1606.04797 (2016). http://arxiv.org/abs/1606.04797

  12. Mobiny, A., Nguyen, H.V., Moulik, S., Garg, N., Wu, C.C.: Dropconnect is effective in modeling uncertainty of Bayesian deep networks. CoRR abs/1906.04569 (2019). http://arxiv.org/abs/1906.04569

  13. Mobiny, A., Singh, A., Van Nguyen, H.: Risk-aware machine learning classifier for skin lesion diagnosis. J. Clin. Med. 8(8), 1241 (2019)

    CrossRef  Google Scholar 

  14. Neal, R.M.: Bayesian Learning for Neural Networks. Springer, Heidelberg (1996). https://doi.org/10.1007/978-1-4612-0745-0

    CrossRef  MATH  Google Scholar 

  15. 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_28http://lmb.informatik.uni-freiburg.de/Publications/2015/RFB15a

    CrossRef  Google Scholar 

  16. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

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Correspondence to Bisser Raytchev .

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Iwamoto, S., Raytchev, B., Tamaki, T., Kaneda, K. (2021). Improving the Reliability of Semantic Segmentation of Medical Images by Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning. In: , et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. UNSURE PIPPI 2021 2021. Lecture Notes in Computer Science(), vol 12959. Springer, Cham. https://doi.org/10.1007/978-3-030-87735-4_4

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  • DOI: https://doi.org/10.1007/978-3-030-87735-4_4

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