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

arXiv: https://arxiv.org/abs/1902.10785 · HTML: https://mlsanity.com/paper/1902.10785/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [Multimodal Representation Learning via Maximization of Local Mutual Information](https://mlsanity.com/paper/2103.04537/) (2021)
- [Image Classification with Consistent Supporting Evidence](https://mlsanity.com/paper/2111.07048/) (2021)

## Abstract (arXiv)

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.