Abstract
Multispectral optical imaging is becoming a key tool in the operating room. Recent research has shown that machine learning algorithms can be used to convert pixel-wise reflectance measurements to tissue parameters, such as oxygenation. However, the accuracy of these algorithms can only be guaranteed if the spectra acquired during surgery match the ones seen during training. It is therefore of great interest to detect so-called out of distribution (OoD) spectra to prevent the algorithm from presenting spurious results. In this paper we present an information theory based approach to OoD detection based on the widely applicable information criterion (WAIC). Our work builds upon recent methodology related to invertible neural networks (INN). Specifically, we make use of an ensemble of INNs as we need their tractable Jacobians in order to compute the WAIC. Comprehensive experiments with in silico, and in vivo multispectral imaging data indicate that our approach is well-suited for OoD detection. Our method could thus be an important step towards reliable functional imaging in the operating room.
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Notes
- 1.
Please note that the sign convention of WAIC of Choi and Watanabe are opposite. We chose Watanabe’s definition.
References
Adler, T.J., et al.: Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks. Int. J. Comput. Assist. Radiol. Surg. (2019)
Ardizzone, L., Kruse, J., Rother, C., Köthe, U.: Analyzing inverse problems with invertible neural networks. In: International Conference on Learning Representations (2019)
Choi, H., Jang, E., Alemi, A.A.: Waic, but why? Generative ensembles for robust anomaly detection. CoRR (2018)
Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using Real NVP. CoRR (2016)
Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing Model Uncertainty in deep learning (2016)
Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kohl, S.A.A., et al.: A probabilistic U-Net for segmentation of ambiguous images (2018)
Leibig, C., Allken, V., Ayhan, M.S., Berens, P., Wahl, S.: Leveraging uncertainty information from deep neural networks for disease detection. Sci. Rep. (2017)
Markou, M., Singh, S.: Novelty detection: a reviewpart 1: statistical approaches. Sig. Process. (2003)
Walter, R.: Real and Complex Analysis (1987)
Watanabe, S.: Algebraic Geometry and Statistical Learning Theory. Cambridge University Press, Cambridge (2009)
Wirkert, S.J., et al.: Robust near real-time estimation of physiological parameters from megapixel multispectral images with inverse Monte Carlo and random forest regression. Int. J. Comput. Assist. Radiol. Surg. (2016)
Wirkert, S.J., et al.: Physiological parameter estimation from multispectral images unleashed. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 134–141. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_16
Zhu, Y., Zabaras, N.: Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification. J. Comput. Phys. (2018)
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Adler, T.J. et al. (2019). Out of Distribution Detection for Intra-operative Functional Imaging. In: Greenspan, H., et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures. CLIP UNSURE 2019 2019. Lecture Notes in Computer Science(), vol 11840. Springer, Cham. https://doi.org/10.1007/978-3-030-32689-0_8
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