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
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Acceso en línea:https://doaj.org/article/61ed017906cc4495a8c8bb0823495394
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Sumario: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.