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Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey

Open-source research software has proven indispensable in modern biomedical image analysis. A multitude of open-source platforms drive image analysis pipelines and help disseminate novel analytical approaches and algorithms. Recent advances in machine learning allow for unprecedented improvement in...

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Autores principales: Li, Rui, Sharma, Vaibhav, Thangamani, Subasini, Yakimovich, Artur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580903/
https://www.ncbi.nlm.nih.gov/pubmed/36304285
http://dx.doi.org/10.3389/fbinf.2022.912809
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author Li, Rui
Sharma, Vaibhav
Thangamani, Subasini
Yakimovich, Artur
author_facet Li, Rui
Sharma, Vaibhav
Thangamani, Subasini
Yakimovich, Artur
author_sort Li, Rui
collection PubMed
description Open-source research software has proven indispensable in modern biomedical image analysis. A multitude of open-source platforms drive image analysis pipelines and help disseminate novel analytical approaches and algorithms. Recent advances in machine learning allow for unprecedented improvement in these approaches. However, these novel algorithms come with new requirements in order to remain open source. To understand how these requirements are met, we have collected 50 biomedical image analysis models and performed a meta-analysis of their respective papers, source code, dataset, and trained model parameters. We concluded that while there are many positive trends in openness, only a fraction of all publications makes all necessary elements available to the research community.
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spelling pubmed-95809032022-10-26 Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey Li, Rui Sharma, Vaibhav Thangamani, Subasini Yakimovich, Artur Front Bioinform Bioinformatics Open-source research software has proven indispensable in modern biomedical image analysis. A multitude of open-source platforms drive image analysis pipelines and help disseminate novel analytical approaches and algorithms. Recent advances in machine learning allow for unprecedented improvement in these approaches. However, these novel algorithms come with new requirements in order to remain open source. To understand how these requirements are met, we have collected 50 biomedical image analysis models and performed a meta-analysis of their respective papers, source code, dataset, and trained model parameters. We concluded that while there are many positive trends in openness, only a fraction of all publications makes all necessary elements available to the research community. Frontiers Media S.A. 2022-07-05 /pmc/articles/PMC9580903/ /pubmed/36304285 http://dx.doi.org/10.3389/fbinf.2022.912809 Text en Copyright © 2022 Li, Sharma, Thangamani and Yakimovich. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioinformatics
Li, Rui
Sharma, Vaibhav
Thangamani, Subasini
Yakimovich, Artur
Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey
title Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey
title_full Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey
title_fullStr Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey
title_full_unstemmed Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey
title_short Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey
title_sort open-source biomedical image analysis models: a meta-analysis and continuous survey
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580903/
https://www.ncbi.nlm.nih.gov/pubmed/36304285
http://dx.doi.org/10.3389/fbinf.2022.912809
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