Learning Decision Ensemble using a Graph Neural Network for Comorbidity Aware Chest Radiograph Screening
This work addresses the need for more accurate automated screening tools for radiologists by considering disease dependencies, though it is incremental as it builds on existing ensemble methods.
The paper tackled the problem of improving chest radiograph screening by incorporating disease comorbidity into ensemble predictions, achieving an average AUC of 0.821 across thirteen diseases using a GNN-based ensemble of DenseNet121.
Chest radiographs are primarily employed for the screening of cardio, thoracic and pulmonary conditions. Machine learning based automated solutions are being developed to reduce the burden of routine screening on Radiologists, allowing them to focus on critical cases. While recent efforts demonstrate the use of ensemble of deep convolutional neural networks(CNN), they do not take disease comorbidity into consideration, thus lowering their screening performance. To address this issue, we propose a Graph Neural Network (GNN) based solution to obtain ensemble predictions which models the dependencies between different diseases. A comprehensive evaluation of the proposed method demonstrated its potential by improving the performance over standard ensembling technique across a wide range of ensemble constructions. The best performance was achieved using the GNN ensemble of DenseNet121 with an average AUC of 0.821 across thirteen disease comorbidities.