Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images.

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 image 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 generative model that includes an auto-encoder for learning a latent representation from the entire image dataset and a classifier that employs this representation for predicting pulmonary edema severity. We use segmentation to focus the auto-encoder on the lungs, where most of pulmonary edema findings are observed. Our experimental results suggest that modeling the distribution of images and providing anatomical information improve 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.