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Bayesian deep learning for accelerated mr image reconstruction

Schlemper J, Castro DC, Bai W, Qin C, Oktay O, Duan J, Price AN, Hajnal J, Rueckert D (2018)

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Publication Type: Conference contribution

Publication year: 2018

Journal

Publisher: Springer Verlag

Book Volume: 11074 LNCS

Pages Range: 64-71

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Granada, ESP

ISBN: 9783030001285

DOI: 10.1007/978-3-030-00129-2_8

Abstract

Recently, many deep learning (DL) based MR image reconstruction methods have been proposed with promising results. However, only a handful of work has been focussing on characterising the behaviour of deep networks, such as investigating when the networks may fail to reconstruct. In this work, we explore the applicability of Bayesian DL techniques to model the uncertainty associated with DL-based reconstructions. In particular, we apply MC-dropout and heteroscedastic loss to the reconstruction networks to model epistemic and aleatoric uncertainty. We show that the proposed Bayesian methods achieve competitive performance when the test images are relatively far from the training data distribution and outperforms when the baseline method is over-parametrised. In addition, we qualitatively show that there seems to be a correlation between the magnitude of the produced uncertainty maps and the error maps, demonstrating the potential utility of the Bayesian DL methods for assessing the reliability of the reconstructed images.

Involved external institutions

How to cite

APA:

Schlemper, J., Castro, D.C., Bai, W., Qin, C., Oktay, O., Duan, J.,... Rueckert, D. (2018). Bayesian deep learning for accelerated mr image reconstruction. In Florian Knoll, Andreas Maier, Daniel Rueckert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 64-71). Granada, ESP: Springer Verlag.

MLA:

Schlemper, Jo, et al. "Bayesian deep learning for accelerated mr image reconstruction." Proceedings of the 1st Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2018 Held in Conjunction with 21st Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Florian Knoll, Andreas Maier, Daniel Rueckert, Springer Verlag, 2018. 64-71.

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