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MIA is an open-source standalone deep learning application for microscopic image analysis
In recent years, the amount of data generated by imaging techniques has grown rapidly, along with increasing computational power and the development of deep learning algorithms. To address the need for powerful automated image analysis tools for a broad range of applications in the biomedical scienc...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Elsevier
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10391334/ https://www.ncbi.nlm.nih.gov/pubmed/37533647 http://dx.doi.org/10.1016/j.crmeth.2023.100517 |
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author | Körber, Nils |
author_facet | Körber, Nils |
author_sort | Körber, Nils |
collection | PubMed |
description | In recent years, the amount of data generated by imaging techniques has grown rapidly, along with increasing computational power and the development of deep learning algorithms. To address the need for powerful automated image analysis tools for a broad range of applications in the biomedical sciences, the Microscopic Image Analyzer (MIA) was developed. MIA combines a graphical user interface that obviates the need for programming skills with state-of-the-art deep-learning algorithms for segmentation, object detection, and classification. It runs as a standalone, platform-independent application and uses open data formats, which are compatible with commonly used open-source software packages. The software provides a unified interface for easy image labeling, model training, and inference. Furthermore, the software was evaluated in a public competition and performed among the top three for all tested datasets. |
format | Online Article Text |
id | pubmed-10391334 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-103913342023-08-02 MIA is an open-source standalone deep learning application for microscopic image analysis Körber, Nils Cell Rep Methods Report In recent years, the amount of data generated by imaging techniques has grown rapidly, along with increasing computational power and the development of deep learning algorithms. To address the need for powerful automated image analysis tools for a broad range of applications in the biomedical sciences, the Microscopic Image Analyzer (MIA) was developed. MIA combines a graphical user interface that obviates the need for programming skills with state-of-the-art deep-learning algorithms for segmentation, object detection, and classification. It runs as a standalone, platform-independent application and uses open data formats, which are compatible with commonly used open-source software packages. The software provides a unified interface for easy image labeling, model training, and inference. Furthermore, the software was evaluated in a public competition and performed among the top three for all tested datasets. Elsevier 2023-06-26 /pmc/articles/PMC10391334/ /pubmed/37533647 http://dx.doi.org/10.1016/j.crmeth.2023.100517 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Report Körber, Nils MIA is an open-source standalone deep learning application for microscopic image analysis |
title | MIA is an open-source standalone deep learning application for microscopic image analysis |
title_full | MIA is an open-source standalone deep learning application for microscopic image analysis |
title_fullStr | MIA is an open-source standalone deep learning application for microscopic image analysis |
title_full_unstemmed | MIA is an open-source standalone deep learning application for microscopic image analysis |
title_short | MIA is an open-source standalone deep learning application for microscopic image analysis |
title_sort | mia is an open-source standalone deep learning application for microscopic image analysis |
topic | Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10391334/ https://www.ncbi.nlm.nih.gov/pubmed/37533647 http://dx.doi.org/10.1016/j.crmeth.2023.100517 |
work_keys_str_mv | AT korbernils miaisanopensourcestandalonedeeplearningapplicationformicroscopicimageanalysis |