  Aortic stenosis (AS) is a degenerative valve condition that causes
substantial morbidity and mortality. This condition is under-diagnosed and
under-treated. In clinical practice, AS is diagnosed with expert review of
transthoracic echocardiography, which produces dozens of ultrasound images of
the heart. Only some of these views show the aortic valve. To automate
screening for AS, deep networks must learn to mimic a human expert's ability to
identify views of the aortic valve then aggregate across these relevant images
to produce a study-level diagnosis. We find previous approaches to AS detection
yield insufficient accuracy due to relying on inflexible averages across
images. We further find that off-the-shelf attention-based multiple instance
learning (MIL) performs poorly. We contribute a new end-to-end MIL approach
with two key methodological innovations. First, a supervised attention
technique guides the learned attention mechanism to favor relevant views.
Second, a novel self-supervised pretraining strategy applies contrastive
learning on the representation of the whole study instead of individual images
as commonly done in prior literature. Experiments on an open-access dataset and
an external validation set show that our approach yields higher accuracy while
reducing model size.
