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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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Detalles Bibliográficos
Autor principal: Körber, Nils
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
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.
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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
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