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Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning

Histopathological diagnosis of lymphomas represents a challenge requiring either expertise or centralised review, and greatly depends on the technical process of tissue sections. Hence, we developed an innovative deep-learning framework, empowered with a certainty estimation level, designed for haem...

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Autores principales: Syrykh, Charlotte, Abreu, Arnaud, Amara, Nadia, Siegfried, Aurore, Maisongrosse, Véronique, Frenois, François X., Martin, Laurent, Rossi, Cédric, Laurent, Camille, Brousset, Pierre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195401/
https://www.ncbi.nlm.nih.gov/pubmed/32377574
http://dx.doi.org/10.1038/s41746-020-0272-0
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author Syrykh, Charlotte
Abreu, Arnaud
Amara, Nadia
Siegfried, Aurore
Maisongrosse, Véronique
Frenois, François X.
Martin, Laurent
Rossi, Cédric
Laurent, Camille
Brousset, Pierre
author_facet Syrykh, Charlotte
Abreu, Arnaud
Amara, Nadia
Siegfried, Aurore
Maisongrosse, Véronique
Frenois, François X.
Martin, Laurent
Rossi, Cédric
Laurent, Camille
Brousset, Pierre
author_sort Syrykh, Charlotte
collection PubMed
description Histopathological diagnosis of lymphomas represents a challenge requiring either expertise or centralised review, and greatly depends on the technical process of tissue sections. Hence, we developed an innovative deep-learning framework, empowered with a certainty estimation level, designed for haematoxylin and eosin-stained slides analysis, with special focus on follicular lymphoma (FL) diagnosis. Whole-slide images of lymph nodes affected by FL or follicular hyperplasia were used for training, validating, and finally testing Bayesian neural networks (BNN). These BNN provide a diagnostic prediction coupled with an effective certainty estimation, and generate accurate diagnosis with an area under the curve reaching 0.99. Through its uncertainty estimation, our network is also able to detect unfamiliar data such as other small B cell lymphomas or technically heterogeneous cases from external centres. We demonstrate that machine-learning techniques are sensitive to the pre-processing of histopathology slides and require appropriate training to build universal tools to aid diagnosis.
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spelling pubmed-71954012020-05-06 Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning Syrykh, Charlotte Abreu, Arnaud Amara, Nadia Siegfried, Aurore Maisongrosse, Véronique Frenois, François X. Martin, Laurent Rossi, Cédric Laurent, Camille Brousset, Pierre NPJ Digit Med Article Histopathological diagnosis of lymphomas represents a challenge requiring either expertise or centralised review, and greatly depends on the technical process of tissue sections. Hence, we developed an innovative deep-learning framework, empowered with a certainty estimation level, designed for haematoxylin and eosin-stained slides analysis, with special focus on follicular lymphoma (FL) diagnosis. Whole-slide images of lymph nodes affected by FL or follicular hyperplasia were used for training, validating, and finally testing Bayesian neural networks (BNN). These BNN provide a diagnostic prediction coupled with an effective certainty estimation, and generate accurate diagnosis with an area under the curve reaching 0.99. Through its uncertainty estimation, our network is also able to detect unfamiliar data such as other small B cell lymphomas or technically heterogeneous cases from external centres. We demonstrate that machine-learning techniques are sensitive to the pre-processing of histopathology slides and require appropriate training to build universal tools to aid diagnosis. Nature Publishing Group UK 2020-05-01 /pmc/articles/PMC7195401/ /pubmed/32377574 http://dx.doi.org/10.1038/s41746-020-0272-0 Text en © The Author(s) 2020 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Syrykh, Charlotte
Abreu, Arnaud
Amara, Nadia
Siegfried, Aurore
Maisongrosse, Véronique
Frenois, François X.
Martin, Laurent
Rossi, Cédric
Laurent, Camille
Brousset, Pierre
Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning
title Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning
title_full Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning
title_fullStr Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning
title_full_unstemmed Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning
title_short Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning
title_sort accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195401/
https://www.ncbi.nlm.nih.gov/pubmed/32377574
http://dx.doi.org/10.1038/s41746-020-0272-0
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