Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment.

ABSTRACT: We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiographs and free-text radiology reports exist, only limited numerical edema severity labels can be extracted from radiology reports. This is a significant challenge in learning such models for image classification. To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time. Our experimental results suggest that the joint image-text representation learning improves the performance of pulmonary edema assessment compared to a supervised model traine

SUBMITTER: Chauhan G 

PROVIDER: S-EPMC7901713 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature