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

Abstract 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...

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Autores principales:	Charlotte Syrykh, Arnaud Abreu, Nadia Amara, Aurore Siegfried, Véronique Maisongrosse, François X. Frenois, Laurent Martin, Cédric Rossi, Camille Laurent, Pierre Brousset
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/7538dbd0eeaf410ea2c52f6970ce89a3
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spelling	oai:doaj.org-article:7538dbd0eeaf410ea2c52f6970ce89a32021-12-02T16:55:35ZAccurate diagnosis of lymphoma on whole-slide histopathology images using deep learning10.1038/s41746-020-0272-02398-6352https://doaj.org/article/7538dbd0eeaf410ea2c52f6970ce89a32020-05-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0272-0https://doaj.org/toc/2398-6352Abstract 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.Charlotte SyrykhArnaud AbreuNadia AmaraAurore SiegfriedVéronique MaisongrosseFrançois X. FrenoisLaurent MartinCédric RossiCamille LaurentPierre BroussetNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Charlotte Syrykh Arnaud Abreu Nadia Amara Aurore Siegfried Véronique Maisongrosse François X. Frenois Laurent Martin Cédric Rossi Camille Laurent Pierre Brousset Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning
description	Abstract 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	article
author	Charlotte Syrykh Arnaud Abreu Nadia Amara Aurore Siegfried Véronique Maisongrosse François X. Frenois Laurent Martin Cédric Rossi Camille Laurent Pierre Brousset
author_facet	Charlotte Syrykh Arnaud Abreu Nadia Amara Aurore Siegfried Véronique Maisongrosse François X. Frenois Laurent Martin Cédric Rossi Camille Laurent Pierre Brousset
author_sort	Charlotte Syrykh
title	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_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_sort	accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/7538dbd0eeaf410ea2c52f6970ce89a3
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Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning

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