Value of Public Challenges for the Development of Pathology Deep Learning Algorithms

The introduction of digital pathology is changing the practice of diagnostic anatomic pathology. Digital pathology offers numerous advantages over using a physical slide on a physical microscope, including more discriminative tools to render a more precise diagnostic report. The development of these...

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Detalles Bibliográficos
Autores principales: Hartman, Douglas Joseph, Van Der Laak, Jeroen A. W. M., Gurcan, Metin N., Pantanowitz, Liron
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2020
Materias:
Editorial
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147520/
https://www.ncbi.nlm.nih.gov/pubmed/32318315
http://dx.doi.org/10.4103/jpi.jpi_64_19
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author Hartman, Douglas Joseph
Van Der Laak, Jeroen A. W. M.
Gurcan, Metin N.
Pantanowitz, Liron
author_facet Hartman, Douglas Joseph
Van Der Laak, Jeroen A. W. M.
Gurcan, Metin N.
Pantanowitz, Liron
author_sort Hartman, Douglas Joseph
collection PubMed
description The introduction of digital pathology is changing the practice of diagnostic anatomic pathology. Digital pathology offers numerous advantages over using a physical slide on a physical microscope, including more discriminative tools to render a more precise diagnostic report. The development of these tools is being facilitated by public challenges related to specific diagnostic tasks within anatomic pathology. To date, 24 public challenges related to pathology tasks have been published. This article discusses these public challenges and briefly reviews the underlying characteristics of public challenges and why they are helpful to the development of digital tools.
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spelling pubmed-71475202020-04-21 Value of Public Challenges for the Development of Pathology Deep Learning Algorithms Hartman, Douglas Joseph Van Der Laak, Jeroen A. W. M. Gurcan, Metin N. Pantanowitz, Liron J Pathol Inform Editorial The introduction of digital pathology is changing the practice of diagnostic anatomic pathology. Digital pathology offers numerous advantages over using a physical slide on a physical microscope, including more discriminative tools to render a more precise diagnostic report. The development of these tools is being facilitated by public challenges related to specific diagnostic tasks within anatomic pathology. To date, 24 public challenges related to pathology tasks have been published. This article discusses these public challenges and briefly reviews the underlying characteristics of public challenges and why they are helpful to the development of digital tools. Wolters Kluwer - Medknow 2020-02-26 /pmc/articles/PMC7147520/ /pubmed/32318315 http://dx.doi.org/10.4103/jpi.jpi_64_19 Text en Copyright: © 2020 Journal of Pathology Informatics http://creativecommons.org/licenses/by-nc-sa/4.0 This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Editorial
Hartman, Douglas Joseph
Van Der Laak, Jeroen A. W. M.
Gurcan, Metin N.
Pantanowitz, Liron
Value of Public Challenges for the Development of Pathology Deep Learning Algorithms
title Value of Public Challenges for the Development of Pathology Deep Learning Algorithms
title_full Value of Public Challenges for the Development of Pathology Deep Learning Algorithms
title_fullStr Value of Public Challenges for the Development of Pathology Deep Learning Algorithms
title_full_unstemmed Value of Public Challenges for the Development of Pathology Deep Learning Algorithms
title_short Value of Public Challenges for the Development of Pathology Deep Learning Algorithms
title_sort value of public challenges for the development of pathology deep learning algorithms
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147520/
https://www.ncbi.nlm.nih.gov/pubmed/32318315
http://dx.doi.org/10.4103/jpi.jpi_64_19
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