Detection of Foreign Objects in Chest Radiographs using Deep Learning

We propose a deep learning framework for the automated detection of foreign objects in chest radiographs. Foreign objects can affect the diagnostic quality of an image and could affect the performance of CAD systems. Their automated detection could alert the technologists to take corrective actions. In addition, the detection of foreign objects such as pacemakers or placed devices could also help automate clinical workflow. We used a subset of the MIMIC CXR dataset and annotated 6061 images for six foreign object categories namely tubes and wires, pacemakers, implants, small external objects, jewelry and push-buttons. A transfer learning based approach was developed for both binary and multi-label classification. All networks were pre-trained using the computer vision database ImageNet and the NIH database ChestX-ray14. The evaluation was performed using 5-fold cross-validation (CV) and an additional test set with 1357 images. We achieved the best average area under the ROC curve (AUC) of 0.972 for binary classification and 0.969 for multilabel classification using 5-fold CV. On the test dataset, the respective best AUCs of 0.984 and 0.969 were obtained using a dense convolutional network.