Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification

Convolutional Neural Networks are powerful tools for clinical diagnosis but their effectiveness decreases when the number of available samples is small. Here, the authors develop a cumulative learning method by training the same model through several classification tasks over various small Mass Spec...

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Autores principales:	Khawla Seddiki, Philippe Saudemont, Frédéric Precioso, Nina Ogrinc, Maxence Wisztorski, Michel Salzet, Isabelle Fournier, Arnaud Droit
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/61ed017906cc4495a8c8bb0823495394
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id	oai:doaj.org-article:61ed017906cc4495a8c8bb0823495394
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spelling	oai:doaj.org-article:61ed017906cc4495a8c8bb08234953942021-12-02T15:39:22ZCumulative learning enables convolutional neural network representations for small mass spectrometry data classification10.1038/s41467-020-19354-z2041-1723https://doaj.org/article/61ed017906cc4495a8c8bb08234953942020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19354-zhttps://doaj.org/toc/2041-1723Convolutional Neural Networks are powerful tools for clinical diagnosis but their effectiveness decreases when the number of available samples is small. Here, the authors develop a cumulative learning method by training the same model through several classification tasks over various small Mass Spectrometry datasets.Khawla SeddikiPhilippe SaudemontFrédéric PreciosoNina OgrincMaxence WisztorskiMichel SalzetIsabelle FournierArnaud DroitNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Khawla Seddiki Philippe Saudemont Frédéric Precioso Nina Ogrinc Maxence Wisztorski Michel Salzet Isabelle Fournier Arnaud Droit Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification
description	Convolutional Neural Networks are powerful tools for clinical diagnosis but their effectiveness decreases when the number of available samples is small. Here, the authors develop a cumulative learning method by training the same model through several classification tasks over various small Mass Spectrometry datasets.
format	article
author	Khawla Seddiki Philippe Saudemont Frédéric Precioso Nina Ogrinc Maxence Wisztorski Michel Salzet Isabelle Fournier Arnaud Droit
author_facet	Khawla Seddiki Philippe Saudemont Frédéric Precioso Nina Ogrinc Maxence Wisztorski Michel Salzet Isabelle Fournier Arnaud Droit
author_sort	Khawla Seddiki
title	Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification
title_short	Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification
title_full	Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification
title_fullStr	Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification
title_full_unstemmed	Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification
title_sort	cumulative learning enables convolutional neural network representations for small mass spectrometry data classification
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/61ed017906cc4495a8c8bb0823495394
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Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification

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