  Despite the progress in utilizing deep learning to automate chest radiograph
interpretation and disease diagnosis tasks, change between sequential Chest
X-rays (CXRs) has received limited attention. Monitoring the progression of
pathologies that are visualized through chest imaging poses several challenges
in anatomical motion estimation and image registration, i.e., spatially
aligning the two images and modeling temporal dynamics in change detection. In
this work, we propose CheXRelNet, a neural model that can track longitudinal
pathology change relations between two CXRs. CheXRelNet incorporates local and
global visual features, utilizes inter-image and intra-image anatomical
information, and learns dependencies between anatomical region attributes, to
accurately predict disease change for a pair of CXRs. Experimental results on
the Chest ImaGenome dataset show increased downstream performance compared to
baselines. Code is available at https://github.com/PLAN-Lab/ChexRelNet
