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

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 and James R. Priest
Additional contact information
Jason A. Fries: Stanford University
Paroma Varma: Stanford University
Vincent S. Chen: Stanford University
Ke Xiao: Stanford University
Heliodoro Tejeda: Stanford University
Priyanka Saha: Stanford University
Jared Dunnmon: Stanford University
Henry Chubb: Stanford University
Shiraz Maskatia: Stanford University
Madalina Fiterau: Stanford University
Scott Delp: Stanford University
Euan Ashley: Stanford University
Christopher Ré: Stanford University
James R. Priest: Stanford University

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled, which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep learning model for classification of aortic valve malformations using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For aortic valve classification, models trained with imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs. In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes a deep learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.

Date: 2019
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DOI: 10.1038/s41467-019-11012-3

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