DP-GAT: A Framework for Image-based Disease Progression Prediction

Alex Foo, Wynne Hsu, Mong-Li Lee, Gavin Siew Wei Tan

Abstract: Predicting disease progression is key to provide stratified patient care and enable good utilization of healthcare resources. The availability of longitudinal images has enabled image-based disease progression prediction. In this work, we propose a framework called DP-GAT to identify regions containing significant biological structures and model the relationships among these regions as a graph along with their respective contexts. We perform reasoning via Graph Attention Network to generate representations that enable accurate disease progression prediction. We further extend DP-GAT to perform 3D medical volume segmentation. Experiments on real world medical image datasets demonstrate the advantage of our approach over strong baseline methods for both disease progression prediction and 3D segmentation tasks.