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Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8303613/ https://www.ncbi.nlm.nih.gov/pubmed/34359296 http://dx.doi.org/10.3390/diagnostics11071213 |
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author | Jue, Joshua S. Mikhail, David González, Javier Alameddine, Mahmoud |
author_facet | Jue, Joshua S. Mikhail, David González, Javier Alameddine, Mahmoud |
author_sort | Jue, Joshua S. |
collection | PubMed |
description | |
format | Online Article Text |
id | pubmed-8303613 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83036132021-07-25 Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335 Jue, Joshua S. Mikhail, David González, Javier Alameddine, Mahmoud Diagnostics (Basel) Comment MDPI 2021-07-05 /pmc/articles/PMC8303613/ /pubmed/34359296 http://dx.doi.org/10.3390/diagnostics11071213 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Comment Jue, Joshua S. Mikhail, David González, Javier Alameddine, Mahmoud Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335 |
title | Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335 |
title_full | Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335 |
title_fullStr | Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335 |
title_full_unstemmed | Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335 |
title_short | Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335 |
title_sort | value of mri to improve deep learning model that identifies high-grade prostate cancer. comment on gentile et al. optimized identification of high-grade prostate cancer by combining different psa molecular forms and psa density in a deep learning model. diagnostics 2021, 11, 335 |
topic | Comment |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8303613/ https://www.ncbi.nlm.nih.gov/pubmed/34359296 http://dx.doi.org/10.3390/diagnostics11071213 |
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