Training confounder-free deep learning models for medical applications

The presence of confounding effects is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Here, the authors introduce an end-to-end approach for deriving features invariant to confounding factors as inputs to prediction models.

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Detalles Bibliográficos
Autores principales:	Qingyu Zhao, Ehsan Adeli, Kilian M. Pohl
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/59077cc4c0464b168994c4db5c2b33d0
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spelling	oai:doaj.org-article:59077cc4c0464b168994c4db5c2b33d02021-12-02T14:40:38ZTraining confounder-free deep learning models for medical applications10.1038/s41467-020-19784-92041-1723https://doaj.org/article/59077cc4c0464b168994c4db5c2b33d02020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19784-9https://doaj.org/toc/2041-1723The presence of confounding effects is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Here, the authors introduce an end-to-end approach for deriving features invariant to confounding factors as inputs to prediction models.Qingyu ZhaoEhsan AdeliKilian M. PohlNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Qingyu Zhao Ehsan Adeli Kilian M. Pohl Training confounder-free deep learning models for medical applications
description	The presence of confounding effects is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Here, the authors introduce an end-to-end approach for deriving features invariant to confounding factors as inputs to prediction models.
format	article
author	Qingyu Zhao Ehsan Adeli Kilian M. Pohl
author_facet	Qingyu Zhao Ehsan Adeli Kilian M. Pohl
author_sort	Qingyu Zhao
title	Training confounder-free deep learning models for medical applications
title_short	Training confounder-free deep learning models for medical applications
title_full	Training confounder-free deep learning models for medical applications
title_fullStr	Training confounder-free deep learning models for medical applications
title_full_unstemmed	Training confounder-free deep learning models for medical applications
title_sort	training confounder-free deep learning models for medical applications
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/59077cc4c0464b168994c4db5c2b33d0
work_keys_str_mv	AT qingyuzhao trainingconfounderfreedeeplearningmodelsformedicalapplications AT ehsanadeli trainingconfounderfreedeeplearningmodelsformedicalapplications AT kilianmpohl trainingconfounderfreedeeplearningmodelsformedicalapplications
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Training confounder-free deep learning models for medical applications

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