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...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Nature Publishing Group 2019-07-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-019-11012-3
Description
Summary: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.
ISSN:2041-1723