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MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Networks

M. Chandler, C. Jenkins, Sophie Shermer Orcid Logo, F. C. Langbein

arXiv

Swansea University Author: Sophie Shermer Orcid Logo

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DOI (Published version): 10.48550/arXiv.1909.03836

Published in: arXiv
Published: 2019
Online Access: https://arxiv.org/abs/1909.03836
URI: https://cronfa.swan.ac.uk/Record/cronfa61076
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first_indexed 2022-10-10T16:13:45Z
last_indexed 2023-01-13T19:21:39Z
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spelling 2022-10-17T13:18:45.8391468 v2 61076 2022-09-06 MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Networks 6ebef22eb31eafc75aedcf5bfe487777 0000-0002-5530-7750 Sophie Shermer Sophie Shermer true false 2022-09-06 SPH Journal Article arXiv 6 9 2019 2019-09-06 10.48550/arXiv.1909.03836 https://arxiv.org/abs/1909.03836 Preprint article before certification by peer review. COLLEGE NANME Physics COLLEGE CODE SPH Swansea University 2022-10-17T13:18:45.8391468 2022-09-06T15:11:39.4096885 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Physics M. Chandler 1 C. Jenkins 2 Sophie Shermer 0000-0002-5530-7750 3 F. C. Langbein 4
title MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Networks
spellingShingle MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Networks
Sophie Shermer
title_short MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Networks
title_full MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Networks
title_fullStr MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Networks
title_full_unstemmed MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Networks
title_sort MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Networks
author_id_str_mv 6ebef22eb31eafc75aedcf5bfe487777
author_id_fullname_str_mv 6ebef22eb31eafc75aedcf5bfe487777_***_Sophie Shermer
author Sophie Shermer
author2 M. Chandler
C. Jenkins
Sophie Shermer
F. C. Langbein
format Journal article
container_title arXiv
publishDate 2019
institution Swansea University
doi_str_mv 10.48550/arXiv.1909.03836
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Physics{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Physics
url https://arxiv.org/abs/1909.03836
document_store_str 0
active_str 0
published_date 2019-09-06T04:19:42Z
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score 11.012678

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