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Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images

Blumberg, SB; Tanno, R; Kokkinos, I; Alexander, DC; (2018) Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images. In: Frangi, AF and Schnabel, JA and Davatzikos, C and Alberola-López, C and Fichtinger, G, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, Proceedings, Part I. (pp. pp. 118-125). Springer: Cham, Switzerland. Green open access

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Abstract

In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning. We exploit memory-efficient backpropagation techniques, to reduce the memory complexity of network training from being linear in the network’s depth, to being roughly constant – permitting us to elongate deep architectures with negligible memory increase. We evaluate our methodology in the paradigm of Image Quality Transfer, whilst noting its potential application to various tasks that use deep learning. We study the impact of depth on accuracy and show that deeper models have more predictive power, which may exploit larger training sets. We obtain substantially better results than the previous state-of-the-art model with a slight memory increase, reducing the root-mean-squared-error by 13%. Our code is publicly available.

Type: Proceedings paper
Title: Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images
Event: MICCAI 2018, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, 16-20 September 2018, Granada, Spain
ISBN-13: 978-3-030-00927-4
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-00928-1_14
Publisher version: https://doi.org/10.1007/978-3-030-00928-1_14
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.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10060970
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