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Bayesian Volumetric Autoregressive generative models for better semisupervised learning

Pombo, G; Gray, R; Varsavsky, T; Ashburner, J; Nachev, P; (2019) Bayesian Volumetric Autoregressive generative models for better semisupervised learning. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 201. (pp. pp. 429-437). Springer Green open access

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Abstract

Deep generative models are rapidly gaining traction in medical imaging. Nonetheless, most generative architectures struggle to capture the underlying probability distributions of volumetric data, exhibit convergence problems, and offer no robust indices of model uncertainty. By comparison, the autoregressive generative model PixelCNN can be extended to volumetric data with relative ease, it readily attempts to learn the true underlying probability distribution and it still admits a Bayesian reformulation that provides a principled framework for reasoning about model uncertainty. Our contributions in this paper are two fold: first, we extend PixelCNN to work with volumetric brain magnetic resonance imaging data. Second, we show that reformulating this model to approximate a deep Gaussian process yields a measure of uncertainty that improves the performance of semi-supervised learning, in particular classification performance in settings where the proportion of labelled data is low. We quantify this improvement across classification, regression, and semantic segmentation tasks, training and testing on clinical magnetic resonance brain imaging data comprising T1-weighted and diffusion-weighted sequences.

Type: Proceedings paper
Title: Bayesian Volumetric Autoregressive generative models for better semisupervised learning
Event: International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 201
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-32251-9_47
Publisher version: https://doi.org/10.1007/978-3-030-32251-9_47
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: generative, semi-supervised, Bayesian, autoregressive
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10079424
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