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Uncertainty Quantification in End-to-End Implicit Neural Representations for Medical Imaging
Bobby He · Francisca Vasconcelos · Yee Whye Teh

Implicit neural representations (INRs) have recently achieved impressive results in image representation. This work explores the uncertainty quantification quality of INRs for medical imaging. We propose the first uncertainty aware, end-to-end INR architecture for computed tomography (CT) image reconstruction. Four established neural network uncertainty quantification techniques -- deep ensembles, Monte Carlo dropout, Bayes-by-backpropagation, and Hamiltonian Monte Carlo -- are implemented and assessed according to both image reconstruction quality and model calibration. We find that these INRs outperform traditional medical image reconstruction algorithms according to predictive accuracy; deep ensembles of Monte Carlo dropout base-learners achieve the best image reconstruction and model calibration among the techniques tested; activation function and random Fourier feature embedding frequency have large effects on model performance; and Bayes-by-backpropogation is ill-suited for sampling from the INR posterior distributions. Preliminary results further indicate that, with adequate tuning, Hamiltonian Monte Carlo may outperform Monte Carlo dropout deep ensembles.

Author Information

Bobby He (University of Oxford)
Francisca Vasconcelos (University of Oxford)
Yee Whye Teh (University of Oxford, DeepMind)

I am a Professor of Statistical Machine Learning at the Department of Statistics, University of Oxford and a Research Scientist at DeepMind. I am also an Alan Turing Institute Fellow and a European Research Council Consolidator Fellow. I obtained my Ph.D. at the University of Toronto (working with Geoffrey Hinton), and did postdoctoral work at the University of California at Berkeley (with Michael Jordan) and National University of Singapore (as Lee Kuan Yew Postdoctoral Fellow). I was a Lecturer then a Reader at the Gatsby Computational Neuroscience Unit, UCL, and a tutorial fellow at University College Oxford, prior to my current appointment. I am interested in the statistical and computational foundations of intelligence, and works on scalable machine learning, probabilistic models, Bayesian nonparametrics and deep learning. I was programme co-chair of ICML 2017 and AISTATS 2010.

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