Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences

The availability of labelled training data is one of the practical obstacles towards wide application of machine learning models in medicine. Here the authors develop a weakly supervised deep learning model for the classification of aortic malformations using unlabelled cardiac MRI sequences from th...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jason A. Fries, Paroma Varma, Vincent S. Chen, Ke Xiao, Heliodoro Tejeda, Priyanka Saha, Jared Dunnmon, Henry Chubb, Shiraz Maskatia, Madalina Fiterau, Scott Delp, Euan Ashley, Christopher Ré, James R. Priest
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/cd0dfcc01b1d4f6ab865ca1d205bea84
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:cd0dfcc01b1d4f6ab865ca1d205bea84
record_format	dspace
spelling	oai:doaj.org-article:cd0dfcc01b1d4f6ab865ca1d205bea842021-12-02T14:38:42ZWeakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences10.1038/s41467-019-11012-32041-1723https://doaj.org/article/cd0dfcc01b1d4f6ab865ca1d205bea842019-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-11012-3https://doaj.org/toc/2041-1723The availability of labelled training data is one of the practical obstacles towards wide application of machine learning models in medicine. Here the authors develop a weakly supervised deep learning model for the classification of aortic malformations using unlabelled cardiac MRI sequences from the UK biobank.Jason A. FriesParoma VarmaVincent S. ChenKe XiaoHeliodoro TejedaPriyanka SahaJared DunnmonHenry ChubbShiraz MaskatiaMadalina FiterauScott DelpEuan AshleyChristopher RéJames R. PriestNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-10 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jason A. Fries Paroma Varma Vincent S. Chen Ke Xiao Heliodoro Tejeda Priyanka Saha Jared Dunnmon Henry Chubb Shiraz Maskatia Madalina Fiterau Scott Delp Euan Ashley Christopher Ré James R. Priest Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences
description	The availability of labelled training data is one of the practical obstacles towards wide application of machine learning models in medicine. Here the authors develop a weakly supervised deep learning model for the classification of aortic malformations using unlabelled cardiac MRI sequences from the UK biobank.
format	article
author	Jason A. Fries Paroma Varma Vincent S. Chen Ke Xiao Heliodoro Tejeda Priyanka Saha Jared Dunnmon Henry Chubb Shiraz Maskatia Madalina Fiterau Scott Delp Euan Ashley Christopher Ré James R. Priest
author_facet	Jason A. Fries Paroma Varma Vincent S. Chen Ke Xiao Heliodoro Tejeda Priyanka Saha Jared Dunnmon Henry Chubb Shiraz Maskatia Madalina Fiterau Scott Delp Euan Ashley Christopher Ré James R. Priest
author_sort	Jason A. Fries
title	Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences
title_short	Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences
title_full	Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences
title_fullStr	Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences
title_full_unstemmed	Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences
title_sort	weakly supervised classification of aortic valve malformations using unlabeled cardiac mri sequences
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/cd0dfcc01b1d4f6ab865ca1d205bea84
work_keys_str_mv	AT jasonafries weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT paromavarma weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT vincentschen weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT kexiao weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT heliodorotejeda weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT priyankasaha weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT jareddunnmon weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT henrychubb weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT shirazmaskatia weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT madalinafiterau weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT scottdelp weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT euanashley weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT christopherre weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences AT jamesrpriest weaklysupervisedclassificationofaorticvalvemalformationsusingunlabeledcardiacmrisequences
_version_	1718390921457827840

Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences

Ejemplares similares