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Chest radiographs are primarily employed for the screening of cardio,thoracic and pulmonary conditions. Machine learning based automated solutionsare being developed to reduce the burden of routine screening on Radiologists,allowing them to focus on critical cases. While recent efforts demonstrate theuse of ensemble of deep convolutional neural networks(CNN), they do not takedisease comorbidity into consideration, thus lowering their screeningperformance. To address this issue, we propose a Graph Neural Network (GNN)based solution to obtain ensemble predictions which models the dependenciesbetween different diseases. A comprehensive evaluation of the proposed methoddemonstrated its potential by improving the performance over standardensembling technique across a wide range of ensemble constructions. The bestperformance was achieved using the GNN ensemble of DenseNet121 with an averageAUC of 0.821 across thirteen disease comorbidities.

Learning Decision Ensemble using a Graph Neural Network for Comorbidity Aware Chest Radiograph Screening