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Learning Decision Ensemble using a Graph Neural Network for Comorbidity Aware Chest Radiograph Screening.
- Source :
-
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2020 Jul; Vol. 2020, pp. 1234-1237. - Publication Year :
- 2020
-
Abstract
- 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.
- Subjects :
- Comorbidity
Radiography
Research
Machine Learning
Neural Networks, Computer
Subjects
Details
- Language :
- English
- ISSN :
- 2694-0604
- Volume :
- 2020
- Database :
- MEDLINE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Publication Type :
- Academic Journal
- Accession number :
- 33018210
- Full Text :
- https://doi.org/10.1109/EMBC44109.2020.9176693