Abstract
Landmark localization is an important step in image analysis, where the clinical definition of a landmark can be ambiguous, leading to a practical necessity for model uncertainty quantification that is rigorous and trustworthy. In this paper, we present the first Bayesian framework using Gaussian processes to capture both dataset-level landmark ambiguity and sample-level model uncertainty. Our proposed two-stage approach includes a deep learning based U-Net for coarse predictions, followed by a convolutional Gaussian process (CGP) for fine-grained predictions with uncertainty estimates, learning covariance matrices rather than using a pre-defined covariance matrix. Our Bayesian approach yields a more rigorous quantification of uncertainty compared to deep learning-based uncertainty estimation techniques, whilst still achieving comparable localization accuracy. Our results suggest that CGPs can better model the inherent uncertainties in landmark localization tasks and provide more reliable confidence estimates, making it a promising direction for future research.
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Acknowledgements
This work was supported by EPSRC (2274702) and the Welcome Trust (215799/Z/19/Z and 205188/Z/16/Z). Thomas M. McDonald would like to thank the Department of Computer Science at The University of Manchester for their financial support.
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Schobs, L., McDonald, T.M., Lu, H. (2023). Bayesian Uncertainty Estimation in Landmark Localization Using Convolutional Gaussian Processes. In: Sudre, C.H., Baumgartner, C.F., Dalca, A., Mehta, R., Qin, C., Wells, W.M. (eds) Uncertainty for Safe Utilization of Machine Learning in Medical Imaging. UNSURE 2023. Lecture Notes in Computer Science, vol 14291. Springer, Cham. https://doi.org/10.1007/978-3-031-44336-7_3
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