  In this work, we describe our approach to pneumonia classification and
localization in chest radiographs. This method uses only \emph{open-source}
deep learning object detection and is based on CoupleNet, a fully convolutional
network which incorporates global and local features for object detection. Our
approach achieves robustness through critical modifications of the training
process and a novel ensembling algorithm which merges bounding boxes from
several models. We tested our detection algorithm tested on a dataset of 3000
chest radiographs as part of the 2018 RSNA Pneumonia Challenge; our solution
was recognized as a winning entry in a contest which attracted more than 1400
participants worldwide.
