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

By J. Fries, Paroma Varma, Vincent S. Chen, K. Xiao, H. Tejeda, Priyanka Saha, Jared A. Dunnmon, H. Chubb, S. Maskatia, Madalina Fiterau and 4 more. 2018

20 citations recorded by the source corpus.

Population-scale biomedical repositories such as the UK Biobank provide unprecedented access to prospectively collected cardiac imaging data, however the majority of these data are unlabeled, creating barriers to their use in supervised machine learning. We developed a weakly supervised deep learning model for Bicuspid Aortic Valve (BAV) classification using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring curated, hand-labeled training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For BAV classification, training models using these imperfect labels substantially outperformed a traditional supervised model trained on hand-labeled MRIs. In a validation experiment using long-term outcome data from the UK Biobank, our classification model identified a subset of individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes the first deep learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using large collections of unlabeled medical images.

Subfields: Artificial Intelligence, Cardiology and Cardiovascular Medicine, Epidemiology