Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis
  Diagnosis
Zhe Huang Xiaowei Yu Benjamin S. Wessler Michael C. Hughes
Automated interpretation of ultrasound imaging of the heart (echocardiograms)
could improve the detection and treatment of aortic stenosis (AS), a deadly
heart disease. However, existing deep learning pipelines for assessing AS from
echocardiograms have two key limitations. First, most methods rely on limited
2D cineloops, thereby ignoring widely available Doppler imaging that contains
important complementary information about pressure gradients and blood flow
abnormalities associated with AS. Second, obtaining labeled data is difficult.
There are often far more unlabeled echocardiogram recordings available, but
these remain underutilized by existing methods. To overcome these limitations,
we introduce Semi-supervised Multimodal Multiple-Instance Learning (SMMIL), a
new deep learning framework for automatic interpretation for structural heart
diseases like AS. When deployed, SMMIL can combine information from two input
modalities, spectral Dopplers and 2D cineloops, to produce a study-level AS
diagnosis. During training, SMMIL can combine a smaller labeled set and an
abundant unlabeled set of both modalities to improve its classifier.
Experiments demonstrate that SMMIL outperforms recent alternatives at 3-level
AS severity classification as well as several clinically relevant AS detection
tasks.