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Practical machine learning for disease diagnosis

Huw Summers Orcid Logo

Cell Reports Methods, Volume: 1, Issue: 6, Start page: 100103

Swansea University Author: Huw Summers Orcid Logo

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DOI (Published version): 10.1016/j.crmeth.2021.100103

Abstract

Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process, implementing network training by using patient-l...

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Published in: Cell Reports Methods
ISSN: 2667-2375
Published: Elsevier BV 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58659
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first_indexed 2021-11-15T10:51:18Z
last_indexed 2023-01-11T14:39:24Z
id cronfa58659
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spelling 2022-12-17T09:39:43.8489744 v2 58659 2021-11-15 Practical machine learning for disease diagnosis a61c15e220837ebfa52648c143769427 0000-0002-0898-5612 Huw Summers Huw Summers true false 2021-11-15 MEDE Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process, implementing network training by using patient-level rather than cell-level disease classification. Journal Article Cell Reports Methods 1 6 100103 Elsevier BV 2667-2375 25 10 2021 2021-10-25 10.1016/j.crmeth.2021.100103 COLLEGE NANME Biomedical Engineering COLLEGE CODE MEDE Swansea University 2022-12-17T09:39:43.8489744 2021-11-15T10:51:04.1653916 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Huw Summers 0000-0002-0898-5612 1 58659__21738__20f5fdae844e4f8abf365072ebd86c09.pdf 58659.pdf 2021-11-30T16:34:07.7766753 Output 426805 application/pdf Version of Record true Copyright: 2021 The Author(s). This is an open access article under the CC BY-NC-ND license true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Practical machine learning for disease diagnosis
spellingShingle Practical machine learning for disease diagnosis
Huw Summers
title_short Practical machine learning for disease diagnosis
title_full Practical machine learning for disease diagnosis
title_fullStr Practical machine learning for disease diagnosis
title_full_unstemmed Practical machine learning for disease diagnosis
title_sort Practical machine learning for disease diagnosis
author_id_str_mv a61c15e220837ebfa52648c143769427
author_id_fullname_str_mv a61c15e220837ebfa52648c143769427_***_Huw Summers
author Huw Summers
author2 Huw Summers
format Journal article
container_title Cell Reports Methods
container_volume 1
container_issue 6
container_start_page 100103
publishDate 2021
institution Swansea University
issn 2667-2375
doi_str_mv 10.1016/j.crmeth.2021.100103
publisher Elsevier BV
college_str Faculty of Science and Engineering
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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 Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
document_store_str 1
active_str 0
description Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process, implementing network training by using patient-level rather than cell-level disease classification.
published_date 2021-10-25T04:15:21Z
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score 11.017218

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