Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows

Recent advances in computer-aided diagnosis, treatment response and prognosis in radiomics and deep learning challenge radiology with requirements for world-wide methodological standards for labeling, preprocessing and image acquisition protocols. The adoption of these standards in the clinical work...

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Autores principales: Cobo, Miriam, Menéndez Fernández-Miranda, Pablo, Bastarrika, Gorka, Lloret Iglesias, Lara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Comment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590396/
https://www.ncbi.nlm.nih.gov/pubmed/37865635
http://dx.doi.org/10.1038/s41597-023-02641-x
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author Cobo, Miriam
Menéndez Fernández-Miranda, Pablo
Bastarrika, Gorka
Lloret Iglesias, Lara
author_facet Cobo, Miriam
Menéndez Fernández-Miranda, Pablo
Bastarrika, Gorka
Lloret Iglesias, Lara
author_sort Cobo, Miriam
collection PubMed
description Recent advances in computer-aided diagnosis, treatment response and prognosis in radiomics and deep learning challenge radiology with requirements for world-wide methodological standards for labeling, preprocessing and image acquisition protocols. The adoption of these standards in the clinical workflows is a necessary step towards generalization and interoperability of radiomics and artificial intelligence algorithms in medical imaging.
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spelling pubmed-105903962023-10-23 Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows Cobo, Miriam Menéndez Fernández-Miranda, Pablo Bastarrika, Gorka Lloret Iglesias, Lara Sci Data Comment Recent advances in computer-aided diagnosis, treatment response and prognosis in radiomics and deep learning challenge radiology with requirements for world-wide methodological standards for labeling, preprocessing and image acquisition protocols. The adoption of these standards in the clinical workflows is a necessary step towards generalization and interoperability of radiomics and artificial intelligence algorithms in medical imaging. Nature Publishing Group UK 2023-10-21 /pmc/articles/PMC10590396/ /pubmed/37865635 http://dx.doi.org/10.1038/s41597-023-02641-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Comment
Cobo, Miriam
Menéndez Fernández-Miranda, Pablo
Bastarrika, Gorka
Lloret Iglesias, Lara
Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows
title Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows
title_full Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows
title_fullStr Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows
title_full_unstemmed Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows
title_short Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows
title_sort enhancing radiomics and deep learning systems through the standardization of medical imaging workflows
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590396/
https://www.ncbi.nlm.nih.gov/pubmed/37865635
http://dx.doi.org/10.1038/s41597-023-02641-x
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