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

Recent releases of population-scale biomedical repositories such as the UK Biobank have enabled unprecedented access to prospectively collected medical imaging data. Applying machine learning methods to analyze these data holds great promise in facilitating new insights into the genetic and epidemiological associations between anatomical structures and human health. However, the majority of these imaging data are unlabeled and deriving insights is hindered by the cost of manually annotating data at sufficient scale to train state-of-the-art deep learning models. In this work, we develop a weakly supervised deep learning model for Bicuspid Aortic Valve (BAV) classification using up to 4,000 unlabeled cardiac MRI sequences, comprising a total of 120,000 images. Instead of requiring manually labeled training data, weak supervision relies on noisy heuristic functions defined by domain experts to automatically generate large-scale, imperfect training sets. By leveraging new theoretical work on coping with label noise, models can use weaker supervision sources than was previously possible. In our BAV models, this approach substantially outperforms a traditional supervised baseline trained on hand-labeled data alone, with a 64% improvement in mean F1 score (37.8 to 61.4) on held out test data. In a validation experiment using 9,230 individuals with MRIs and long-term outcome data from the UK Biobank, applying the best-performing BAV classification model identified a subset of individuals with a 1.8-fold increase in risk of a major adverse cardiac event (p