  Chest x-ray imaging is widely used for the diagnosis of pneumothorax and
there has been significant interest in developing automated methods to assist
in image interpretation. We present an image classification pipeline which
detects pneumothorax as well as the various types of chest tubes that are
commonly used to treat pneumothorax. Our multi-stage algorithm is based on lung
segmentation followed by pneumothorax classification, including classification
of patches that are most likely to contain pneumothorax. This algorithm
achieves state of the art performance for pneumothorax classification on an
open-source benchmark dataset. Unlike previous work, this algorithm shows
comparable performance on data with and without chest tubes and thus has an
improved clinical utility. To evaluate these algorithms in a realistic clinical
scenario, we demonstrate the ability to identify real cases of missed
pneumothorax in a large dataset of chest x-ray studies.
