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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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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. |
format | Online Article Text |
id | pubmed-7195401 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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