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Emerging Advances to Transform Histopathology Using Virtual Staining

In an age where digitization is widespread in clinical and preclinical workflows, pathology is still predominantly practiced by microscopic evaluation of stained tissue specimens affixed on glass slides. Over the last decade, new high throughput digital scanning microscopes have ushered in the era o...

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Detalles Bibliográficos
Autores principales: Rivenson, Yair, de Haan, Kevin, Wallace, W. Dean, Ozcan, Aydogan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AAAS 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521663/
https://www.ncbi.nlm.nih.gov/pubmed/37849966
http://dx.doi.org/10.34133/2020/9647163
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author Rivenson, Yair
de Haan, Kevin
Wallace, W. Dean
Ozcan, Aydogan
author_facet Rivenson, Yair
de Haan, Kevin
Wallace, W. Dean
Ozcan, Aydogan
author_sort Rivenson, Yair
collection PubMed
description In an age where digitization is widespread in clinical and preclinical workflows, pathology is still predominantly practiced by microscopic evaluation of stained tissue specimens affixed on glass slides. Over the last decade, new high throughput digital scanning microscopes have ushered in the era of digital pathology that, along with recent advances in machine vision, have opened up new possibilities for Computer-Aided-Diagnoses. Despite these advances, the high infrastructural costs related to digital pathology and the perception that the digitization process is an additional and nondirectly reimbursable step have challenged its widespread adoption. Here, we discuss how emerging virtual staining technologies and machine learning can help to disrupt the standard histopathology workflow and create new avenues for the diagnostic paradigm that will benefit patients and healthcare systems alike via digital pathology.
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spelling pubmed-105216632023-10-17 Emerging Advances to Transform Histopathology Using Virtual Staining Rivenson, Yair de Haan, Kevin Wallace, W. Dean Ozcan, Aydogan BME Front Review Article In an age where digitization is widespread in clinical and preclinical workflows, pathology is still predominantly practiced by microscopic evaluation of stained tissue specimens affixed on glass slides. Over the last decade, new high throughput digital scanning microscopes have ushered in the era of digital pathology that, along with recent advances in machine vision, have opened up new possibilities for Computer-Aided-Diagnoses. Despite these advances, the high infrastructural costs related to digital pathology and the perception that the digitization process is an additional and nondirectly reimbursable step have challenged its widespread adoption. Here, we discuss how emerging virtual staining technologies and machine learning can help to disrupt the standard histopathology workflow and create new avenues for the diagnostic paradigm that will benefit patients and healthcare systems alike via digital pathology. AAAS 2020-08-25 /pmc/articles/PMC10521663/ /pubmed/37849966 http://dx.doi.org/10.34133/2020/9647163 Text en Copyright © 2020 Yair Rivenson et al. https://creativecommons.org/licenses/by/4.0/Exclusive Licensee Suzhou Institute of Biomedical Engineering and Technology, CAS. Distributed under a Creative Commons Attribution License (CC BY 4.0). (https://creativecommons.org/licenses/by/4.0/)
spellingShingle Review Article
Rivenson, Yair
de Haan, Kevin
Wallace, W. Dean
Ozcan, Aydogan
Emerging Advances to Transform Histopathology Using Virtual Staining
title Emerging Advances to Transform Histopathology Using Virtual Staining
title_full Emerging Advances to Transform Histopathology Using Virtual Staining
title_fullStr Emerging Advances to Transform Histopathology Using Virtual Staining
title_full_unstemmed Emerging Advances to Transform Histopathology Using Virtual Staining
title_short Emerging Advances to Transform Histopathology Using Virtual Staining
title_sort emerging advances to transform histopathology using virtual staining
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521663/
https://www.ncbi.nlm.nih.gov/pubmed/37849966
http://dx.doi.org/10.34133/2020/9647163
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