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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9580903 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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