We propose and demonstrate machine learning algorithms to assess the severity
of pulmonary edema in chest x-ray images of congestive heart failure patients. Accurate assessment of pulmonary edema in heart failure is critical when making
treatment and disposition decisions...
Our work is grounded in a large-scale
clinical dataset of over 300,000 x-ray images with associated radiology
reports. While edema severity labels can be extracted unambiguously from a
small fraction of the radiology reports, accurate annotation is challenging in
most cases. To take advantage of the unlabeled images, we develop a Bayesian
model that includes a variational auto-encoder for learning a latent
representation from the entire image set trained jointly with a regressor that
employs this representation for predicting pulmonary edema severity. Our
experimental results suggest that modeling the distribution of images jointly
with the limited labels improves the accuracy of pulmonary edema scoring
compared to a strictly supervised approach. To the best of our knowledge, this
is the first attempt to employ machine learning algorithms to automatically and
quantitatively assess the severity of pulmonary edema in chest x-ray images.